[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"similar-krishnakumarsekar--awesome-quantum-machine-learning":3,"tool-krishnakumarsekar--awesome-quantum-machine-learning":61},[4,18,26,36,44,53],{"id":5,"name":6,"github_repo":7,"description_zh":8,"stars":9,"difficulty_score":10,"last_commit_at":11,"category_tags":12,"status":17},4358,"openclaw","openclaw\u002Fopenclaw","OpenClaw 是一款专为个人打造的本地化 AI 助手，旨在让你在自己的设备上拥有完全可控的智能伙伴。它打破了传统 AI 助手局限于特定网页或应用的束缚，能够直接接入你日常使用的各类通讯渠道，包括微信、WhatsApp、Telegram、Discord、iMessage 等数十种平台。无论你在哪个聊天软件中发送消息，OpenClaw 都能即时响应，甚至支持在 macOS、iOS 和 Android 设备上进行语音交互，并提供实时的画布渲染功能供你操控。\n\n这款工具主要解决了用户对数据隐私、响应速度以及“始终在线”体验的需求。通过将 AI 部署在本地，用户无需依赖云端服务即可享受快速、私密的智能辅助，真正实现了“你的数据，你做主”。其独特的技术亮点在于强大的网关架构，将控制平面与核心助手分离，确保跨平台通信的流畅性与扩展性。\n\nOpenClaw 非常适合希望构建个性化工作流的技术爱好者、开发者，以及注重隐私保护且不愿被单一生态绑定的普通用户。只要具备基础的终端操作能力（支持 macOS、Linux 及 Windows WSL2），即可通过简单的命令行引导完成部署。如果你渴望拥有一个懂你",349277,3,"2026-04-06T06:32:30",[13,14,15,16],"Agent","开发框架","图像","数据工具","ready",{"id":19,"name":20,"github_repo":21,"description_zh":22,"stars":23,"difficulty_score":10,"last_commit_at":24,"category_tags":25,"status":17},3808,"stable-diffusion-webui","AUTOMATIC1111\u002Fstable-diffusion-webui","stable-diffusion-webui 是一个基于 Gradio 构建的网页版操作界面，旨在让用户能够轻松地在本地运行和使用强大的 Stable Diffusion 图像生成模型。它解决了原始模型依赖命令行、操作门槛高且功能分散的痛点，将复杂的 AI 绘图流程整合进一个直观易用的图形化平台。\n\n无论是希望快速上手的普通创作者、需要精细控制画面细节的设计师，还是想要深入探索模型潜力的开发者与研究人员，都能从中获益。其核心亮点在于极高的功能丰富度：不仅支持文生图、图生图、局部重绘（Inpainting）和外绘（Outpainting）等基础模式，还独创了注意力机制调整、提示词矩阵、负向提示词以及“高清修复”等高级功能。此外，它内置了 GFPGAN 和 CodeFormer 等人脸修复工具，支持多种神经网络放大算法，并允许用户通过插件系统无限扩展能力。即使是显存有限的设备，stable-diffusion-webui 也提供了相应的优化选项，让高质量的 AI 艺术创作变得触手可及。",162132,"2026-04-05T11:01:52",[14,15,13],{"id":27,"name":28,"github_repo":29,"description_zh":30,"stars":31,"difficulty_score":32,"last_commit_at":33,"category_tags":34,"status":17},1381,"everything-claude-code","affaan-m\u002Feverything-claude-code","everything-claude-code 是一套专为 AI 编程助手（如 Claude Code、Codex、Cursor 等）打造的高性能优化系统。它不仅仅是一组配置文件，而是一个经过长期实战打磨的完整框架，旨在解决 AI 代理在实际开发中面临的效率低下、记忆丢失、安全隐患及缺乏持续学习能力等核心痛点。\n\n通过引入技能模块化、直觉增强、记忆持久化机制以及内置的安全扫描功能，everything-claude-code 能显著提升 AI 在复杂任务中的表现，帮助开发者构建更稳定、更智能的生产级 AI 代理。其独特的“研究优先”开发理念和针对 Token 消耗的优化策略，使得模型响应更快、成本更低，同时有效防御潜在的攻击向量。\n\n这套工具特别适合软件开发者、AI 研究人员以及希望深度定制 AI 工作流的技术团队使用。无论您是在构建大型代码库，还是需要 AI 协助进行安全审计与自动化测试，everything-claude-code 都能提供强大的底层支持。作为一个曾荣获 Anthropic 黑客大奖的开源项目，它融合了多语言支持与丰富的实战钩子（hooks），让 AI 真正成长为懂上",153609,2,"2026-04-13T11:34:59",[14,13,35],"语言模型",{"id":37,"name":38,"github_repo":39,"description_zh":40,"stars":41,"difficulty_score":32,"last_commit_at":42,"category_tags":43,"status":17},2271,"ComfyUI","Comfy-Org\u002FComfyUI","ComfyUI 是一款功能强大且高度模块化的视觉 AI 引擎，专为设计和执行复杂的 Stable Diffusion 图像生成流程而打造。它摒弃了传统的代码编写模式，采用直观的节点式流程图界面，让用户通过连接不同的功能模块即可构建个性化的生成管线。\n\n这一设计巧妙解决了高级 AI 绘图工作流配置复杂、灵活性不足的痛点。用户无需具备编程背景，也能自由组合模型、调整参数并实时预览效果，轻松实现从基础文生图到多步骤高清修复等各类复杂任务。ComfyUI 拥有极佳的兼容性，不仅支持 Windows、macOS 和 Linux 全平台，还广泛适配 NVIDIA、AMD、Intel 及苹果 Silicon 等多种硬件架构，并率先支持 SDXL、Flux、SD3 等前沿模型。\n\n无论是希望深入探索算法潜力的研究人员和开发者，还是追求极致创作自由度的设计师与资深 AI 绘画爱好者，ComfyUI 都能提供强大的支持。其独特的模块化架构允许社区不断扩展新功能，使其成为当前最灵活、生态最丰富的开源扩散模型工具之一，帮助用户将创意高效转化为现实。",108322,"2026-04-10T11:39:34",[14,15,13],{"id":45,"name":46,"github_repo":47,"description_zh":48,"stars":49,"difficulty_score":32,"last_commit_at":50,"category_tags":51,"status":17},6121,"gemini-cli","google-gemini\u002Fgemini-cli","gemini-cli 是一款由谷歌推出的开源 AI 命令行工具，它将强大的 Gemini 大模型能力直接集成到用户的终端环境中。对于习惯在命令行工作的开发者而言，它提供了一条从输入提示词到获取模型响应的最短路径，无需切换窗口即可享受智能辅助。\n\n这款工具主要解决了开发过程中频繁上下文切换的痛点，让用户能在熟悉的终端界面内直接完成代码理解、生成、调试以及自动化运维任务。无论是查询大型代码库、根据草图生成应用，还是执行复杂的 Git 操作，gemini-cli 都能通过自然语言指令高效处理。\n\n它特别适合广大软件工程师、DevOps 人员及技术研究人员使用。其核心亮点包括支持高达 100 万 token 的超长上下文窗口，具备出色的逻辑推理能力；内置 Google 搜索、文件操作及 Shell 命令执行等实用工具；更独特的是，它支持 MCP（模型上下文协议），允许用户灵活扩展自定义集成，连接如图像生成等外部能力。此外，个人谷歌账号即可享受免费的额度支持，且项目基于 Apache 2.0 协议完全开源，是提升终端工作效率的理想助手。",100752,"2026-04-10T01:20:03",[52,13,15,14],"插件",{"id":54,"name":55,"github_repo":56,"description_zh":57,"stars":58,"difficulty_score":32,"last_commit_at":59,"category_tags":60,"status":17},4721,"markitdown","microsoft\u002Fmarkitdown","MarkItDown 是一款由微软 AutoGen 团队打造的轻量级 Python 工具，专为将各类文件高效转换为 Markdown 格式而设计。它支持 PDF、Word、Excel、PPT、图片（含 OCR）、音频（含语音转录）、HTML 乃至 YouTube 链接等多种格式的解析，能够精准提取文档中的标题、列表、表格和链接等关键结构信息。\n\n在人工智能应用日益普及的今天，大语言模型（LLM）虽擅长处理文本，却难以直接读取复杂的二进制办公文档。MarkItDown 恰好解决了这一痛点，它将非结构化或半结构化的文件转化为模型“原生理解”且 Token 效率极高的 Markdown 格式，成为连接本地文件与 AI 分析 pipeline 的理想桥梁。此外，它还提供了 MCP（模型上下文协议）服务器，可无缝集成到 Claude Desktop 等 LLM 应用中。\n\n这款工具特别适合开发者、数据科学家及 AI 研究人员使用，尤其是那些需要构建文档检索增强生成（RAG）系统、进行批量文本分析或希望让 AI 助手直接“阅读”本地文件的用户。虽然生成的内容也具备一定可读性，但其核心优势在于为机器",93400,"2026-04-06T19:52:38",[52,14],{"id":62,"github_repo":63,"name":64,"description_en":65,"description_zh":66,"ai_summary_zh":66,"readme_en":67,"readme_zh":68,"quickstart_zh":69,"use_case_zh":70,"hero_image_url":71,"owner_login":72,"owner_name":73,"owner_avatar_url":74,"owner_bio":75,"owner_company":76,"owner_location":76,"owner_email":77,"owner_twitter":76,"owner_website":78,"owner_url":79,"languages":80,"stars":85,"forks":86,"last_commit_at":87,"license":88,"difficulty_score":89,"env_os":90,"env_gpu":91,"env_ram":91,"env_deps":92,"category_tags":95,"github_topics":96,"view_count":32,"oss_zip_url":76,"oss_zip_packed_at":76,"status":17,"created_at":116,"updated_at":117,"faqs":118,"releases":119},7263,"krishnakumarsekar\u002Fawesome-quantum-machine-learning","awesome-quantum-machine-learning","Here you can get all the Quantum Machine learning Basics, Algorithms ,Study Materials ,Projects and the descriptions of the projects around the web","awesome-quantum-machine-learning 是一个精心整理的量子机器学习资源合集，旨在为探索这一前沿交叉领域的用户提供一站式学习入口。它系统性地汇集了从基础理论到实战算法的全方位资料，包括量子力学与量子计算的核心概念、关键数学桥梁（如张量网络、希尔伯特空间）、经典量子算法（如 Shor 算法、Grover 算法）以及相关的开源库和项目案例。\n\n该资源库主要解决了量子机器学习领域知识分散、入门门槛高且缺乏系统路径的痛点。通过将复杂的物理概念（如叠加态、纠缠态）与机器学习任务紧密连接，它帮助用户理清从经典比特到量子比特的转换逻辑，并深入理解变分量子本征求解器等专用算法的原理。\n\n这份清单特别适合人工智能研究人员、量子计算开发者以及对前沿科技感兴趣的高校师生使用。无论是希望快速构建知识体系的新手，还是寻找特定算法实现或理论依据的资深专家，都能从中获益。其独特的技术亮点在于不仅罗列了算法名称，还通过架构图和深度对比图，直观展示了量子内核结构及不同物理概念间的差异，让抽象的量子理论变得更加具象易懂，是进入量子智能时代的优质指南。","# Awesome Quantum Machine Learning [![Awesome](https:\u002F\u002Fcdn.rawgit.com\u002Fsindresorhus\u002Fawesome\u002Fd7305f38d29fed78fa85652e3a63e154dd8e8829\u002Fmedia\u002Fbadge.svg)](https:\u002F\u002Fgithub.com\u002Fsindresorhus\u002Fawesome)\n\nA curated list of awesome quantum machine learning algorithms,study materials,libraries and software (by language).\n\n[![Main Architecture](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fkrishnakumarsekar_awesome-quantum-machine-learning_readme_eb65bb413326.png)](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1611.09347.pdf)\n\n[![Quantum Kernel](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fkrishnakumarsekar_awesome-quantum-machine-learning_readme_6bed26ea92c2.jpg)](https:\u002F\u002Fwww.dwavesys.com\u002Ftutorials\u002Fbackground-reading-series\u002Fquantum-computing-primer)\n\n[![In Depth Physics Comparison](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fkrishnakumarsekar_awesome-quantum-machine-learning_readme_4b920643d9b1.jpg)](https:\u002F\u002Fpatents.google.com\u002Fpatent\u002FUS20060091375)\n\n\n## Table of Contents\n\n\u003C!-- MarkdownTOC depth=4 -->\n\n- [INTRODUCTION](#introduction)\n - [Why Quantum Machine Learning?](#introduction-why-quantum-machine-learning)\n- [BASICS](#basics)\n - [What is Quantum Mechanics?](#basics-what-quantum-mechanics)\n - [What is Quantum Computing?](#basics-what-quantum-computing)\n - [What is Topological Quantum Computing?](#basics-what-topological-quantum-computing)\n - [Quantum Computing vs Classical Computing](#basics-quantum-classical-vs) \n- [QUANTUM COMPUTING](#quantumcomputing)\n - [Atom Structure](#quantumcomputing-atom-structure)\n - [Photon wave](#quantumcomputing-photon-wave)\n - [Electron Fluctuation or spin](#quantumcomputing-elecfluctuation-spin)\n - [States](#quantumcomputing-states)\n - [SuperPosition](#quantumcomputing-superposition)\n - [SuperPosition specific for machine learning(Quantum Walks)](#quantumcomputing-superpostion-machinelearning)\n - [Classical Bit](#quantumcomputing-classicalbit)\n - [Quantum Bit or Qubit or Qbit](#quantumcomputing-qubit)\n - [Basic Gates in Quantum Computing](#quantumcomputing-basicgates)\n - [Quantum Diode](#quantumcomputing-diode)\n - [Quantum Transistor](#quantumcomputing-transistor)\n - [Quantum Processor](#quantumcomputing-processor)\n - [Quantum Registery QRAM](#quantumcomputing-qram) \n - [Quantum Entanglement](#quantumcomputing-entanglement)\n- [QUANTUM COMPUTING MACHINE LEARNING BRIDGE](#qcmlbridge)\n - [Complex Numbers](#qcmlbridge-complexNumbers)\n - [Tensors](#qcmlbridge-tensors)\n - [Tensors Network](#qcmlbridge-tensors-network) \n - [Oracle](#qcmlbridge-oracle)\n - [Hadamard transform](#qcmlbridge-hadamard)\n - [Hilbert Space](#qcmlbridge-hilbert)\n - [eigenvalues and eigenvectors](#qcmlbridge-eigen)\n - [Schr¨odinger Operators](#qcmlbridge-schrodinger)\n - [Quantum lambda calculus](#qcmlbridge-lamda)\n - [Quantum Amplitute Phase](#qcmlbridge-amp-phase)\n - [Qubits Encode and Decode](#qcmlbridge-encode-decode)\n - [convert classical bit to qubit](#qcmlbridge-classical-qubit)\n - [Quantum Dirac and Kets](#qcmlbridge-dirac-ket)\n - [Quantum Complexity](#qcmlbridge-complexity)\n - [Arbitrary State Generation](#qcmlbridge-arbitarystategeneration)\n- [QUANTUM ALGORITHMS](#quantumalgorithms)\n - [Quantum Fourier Transform](#quantumalgorithms-fourier)\n - [Variational-Quantum-Eigensolver](#quantumalgorithms-quantumeigensolver)\n - [Grovers Algorithm](#quantumalgorithms-grover)\n - [Shor's algorithm](#quantumalgorithms-shors)\n - [Hamiltonian Oracle Model](#quantumalgorithms-hamiltonian)\n - [Bernstein-Vazirani Algorithm](#quantumalgorithms-Bernsteinvazirani)\n - [Simon’s Algorithm](#quantumalgorithms-simons)\n - [Deutsch-Jozsa Algorithm](#quantumalgorithms-deutschjozsa)\n - [Gradient Descent](#quantumalgorithms-gradient-descent) \n - [Phase Estimation](#quantumalgorithms-phase-estimation)\n - [Haar Tansform](#quantumalgorithms-haar)\n - [Quantum Ridgelet Transform](#quantumalgorithms-ridgelet)\n - [Quantum NP Problem](#quantumalgorithms-npproblem)\n- [QUANTUM MACHINE LEARNING ALGORITHMS](#quantumalgorithmsml)\n - [Quantum K-Nearest Neighbour](#quantumalgorithmsml-qknn)\n - [Quantum K-Means](#quantumalgorithmsml-kmeans)\n - [Quantum Fuzzy C-Means](#quantumalgorithmsml-qfcm)\n - [Quantum Support Vector Machine](#quantumalgorithmsml-svm)\n - [Quantum Genetic Algorithm](#quantumalgorithmsml-genetic)\n - [Quantum Hidden Morkov Models](#quantumalgorithmsml-hmm)\n - [Quantum state classification with Bayesian methods](#quantumalgorithmsml-bayesian)\n - [Quantum Ant Colony Optimization](#quantumalgorithmsml-antcolony)\n - [Quantum Cellular Automata](#quantumalgorithmsml-caautomata)\n - [Quantum Classification using Principle Component Analysis](#quantumalgorithmsml-pca)\n - [Quantum Inspired Evolutionary Algorithm](#quantumalgorithmsml-evolutionary)\n - [Quantum Approximate Optimization Algorithm](#quantumalgorithmsml-qaoa)\n - [Quantum Elephant Herding Optimization](#quantumalgorithmsml-qeho)\n - [Quantum-behaved Particle Swarm Optimization](#quantumalgorithmsml-qpso)\n - [Quantum Annealing Expectation-Maximization](#quantumalgorithmsml-qaem)\n- [QAUNTUM NEURAL NETWORK](#qnn)\n - [Quantum perceptrons](#qnn-perceptron)\n - [Qurons](#qnn-qurons)\n - [Quantum Auto Encoder](#qnn-autoencoder)\n - [Quantum Annealing](#qnn-annealing)\n - [Photonic Implementation of Quantum Neural Network](#qnn-photonicqnn)\n - [Quantum Feed Forward Neural Network](#qnn-feedforward)\n - [Quantum Boltzman Neural Network](#qnn-boltzman)\n - [Quantum Neural Net Weight Storage](#qnn-weightstorage)\n - [Quantum Upside Down Neural Net](#qnn-upsidedown)\n - [Quantum Hamiltonian Neural Net](#qnn-hamiltoniannet)\n - [QANN](#qnn-qann)\n - [QPN](#qnn-qpn)\n - [SAL](#qnn-sal)\n - [Quantum Hamiltonian Learning](#qnn-hamiltonianlearning)\n - [Compressed Quantum Hamiltonian Learning](#qnn-compressedhamiltonianlearning)\n- [QAUNTUM STATISTICAL DATA ANALYSIS](#quantumstatistics)\n\t- [Quantum Probability Theory](#quantumstatistics-probabilitytheory)\n - [Kolmogorovian Theory](#quantumstatistics-kolmogorovian)\n - [Quantum Measurement Problem](#quantumstatistics-measurementproblem)\n - [Intuitionistic Logic](#quantumstatistics-intuitionistic)\n - [Heyting Algebra](#quantumstatistics-heytingalgebra)\n - [Quantum Filtering](#quantumstatistics-quantumfiltering)\n - [Paradoxes](#quantumstatistics-paradoxes) \n - [Quantum Stochastic Process](#quantumstatistics-stochasticprocess)\n - [Double Negation](#quantumstatistics-doublenegation)\n - [Quantum Stochastic Calculus](#quantumstatistics-stochasticcalculus)\n - [Hamiltonian Calculus](#quantumstatistics-hamiltoniancalculus)\n - [Quantum Ito's Formula](#quantumstatistics-itosformula)\n - [Quantum Stochastic Differential Equations(QSDE)](#quantumstatistics-qsde)\n - [Quantum Stochastic Integration](#quantumstatistics-stochasticintegration)\n - [Itō Integral](#quantumstatistics-itōintegral)\n - [Quasiprobability Distributions](#quantumstatistics-quasiprobabilitydistributions)\n - [Quantum Wiener Processes](#quantumstatistics-quantumwienerprocesses)\n - [Quantum Statistical Ensemble](#quantumstatistics-statisticalensemble)\n \t- [Quantum Density Operator or Density Matrix](#quantumstatistics-densityoperator)\n - [Gibbs Canonical Ensemble](#quantumstatistics-gibbscanonicalensemble)\n - [Quantum Mean](#quantumstatistics-mean)\n - [Quantum Variance](#quantumstatistics-variance)\n - [Envariance](#quantumstatistics-envariance)\n - [Polynomial Optimization](#quantumstatistics-polynomialoptimization)\n - [Quadratic Unconstrained Binary Optimization](#quantumstatistics-qubo)\n - [Quantum Gradient Descent](#quantumstatistics-quantumgradientdescent)\n - [Quantum Based Newton's Method for Constrained Optimization](#quantumstatistics-newtonmethodconstrainedoptimization)\n - [Quantum Based Newton's Method for UnConstrained Optimization](#quantumstatistics-newtonmethodunconstrainedoptimization)\n - [Quantum Ensemble](#quantumstatistics-quantumensemble)\n - [Quantum Topology](#quantumstatistics-quantumtopology)\n - [Quantum Topological Data Analysis](#quantumstatistics-quantumtopologicaldataanalysis)\n - [Quantum Bayesian Hypothesis](#quantumstatistics-quantumbayesianhypothesis)\n - [Quantum Statistical Decision Theory](#quantumstatistics-quantumstatisticaldecisiontheory)\n - [Quantum Minimax Theorem](#quantumstatistics-quantumminimaxtheorem)\n - [Quantum Hunt-Stein Theorem](#quantumstatistics-quantumhuntsteintheorem)\n - [Quantum Locally Asymptotic Normality](#quantumstatistics-quantumlocalasymptoticnormality)\n - [Quantum Ising Model](#quantumstatistics-isingmodel)\n - [Quantum Metropolis Sampling](#quantumstatistics-metropolissampling)\n - [Quantum Monte Carlo Approximation](#quantumstatistics-montecarloapproximation)\n - [Quantum Bootstrapping](#quantumstatistics-bootstrapping)\n - [Quantum Bootstrap Aggregation](#quantumstatistics-bootstrapaggregation)\n - [Quantum Decision Tree Classifier](#quantumstatistics-decisiontreeclassifier)\n - [Quantum Outlier Detection](#quantumstatistics-outlierdetection)\n - [Cholesky-Decomposition for Quantum Chemistry](#quantumstatistics-choleskydecomposition)\n - [Quantum Statistical Inference](#quantumstatistics-quantumstatisticalinference)\n - [Asymptotic Quantum Statistical Inference](#quantumstatistics-quantumstatisticalinferenceasymptotic)\n - [Quantum Gaussian Mixture Modal](#quantumstatistics-qgmm)\n - [Quantum t-design](#quantumstatistics-quantumtdesign)\n - [Quantum Central Limit Theorem](#quantumstatistics-quantumcentrallimittheorem)\n - [Quantum Hypothesis Testing](#quantumstatistics-quantumhypothesistesting)\n - [Quantum Chi-squared and Goodness of Fit Testing](#quantumstatistics-quantumchisquared)\n \t- [Quantum Estimation Theory](#quantumstatistics-quantumestimationtheory)\n - [Quantum Way of Linear Regression](#quantumstatistics-quantumlinearregression)\n - [Asymptotic Properties of Quantum](#quantumstatistics-quantumasymptoticproperties)\n - [Outlier Detection in Quantum Concepts](#quantumstatistics-quantumoutlier)\n- [QAUNTUM ARTIFICIAL INTELLIGENCE](#quantumai)\n\t- [Heuristic Quantum Mechanics](#quantumai-heuristicmechanics)\n - [Consistent Quantum Reasoning](#quantumai-quantumreasoning)\n - [Quantum Reinforcement Learning](#quantumai-reinforcementlearning)\n- [QAUNTUM COMPUTER VISION](#quantumcv)\n- [QUANTUM PROGRAMMING LANGUAGES , TOOLs and SOFTWARES](#qpl)\n - [ALL](#qpl-all)\n- [QUANTUM ALGORITHMS SOURCE CODES , GITHUBS](#quantumsourcecode)\n- [QUANTUM HOT TOPICS](#quantumhottopics)\n - [Quantum Cognition](#quantumhottopics-cognition)\n - [Quantum Camera](#quantumhottopics-camera)\n - [Quantum Mathematics](#quantumhottopics-mathematics)\n - [Quantum Information Processing](#quantumhottopics-informationprocessing)\n - [Quantum Image Processing](#quantumhottopics-imageprocessing)\n - [Quantum Cryptography](#quantumhottopics-cryptography)\n - [Quantum Elastic Search](#quantumhottopics-elasticsearch)\n - [Quantum DNA Computing](#quantumhottopics-dna)\n - [Adiabetic Quantum Computing](#quantumhottopics-adiabetic)\n - [Topological Big Data Anlytics using Quantum](#quantumhottopics-topologicalbigdata)\n - [Hamiltonian Time Based Quantum Computing](#quantumhottopics-hamiltoniancomputing)\n - [Deep Quantum Learning](#quantumhottopics-deepquantumlearning)\n - [Quantum Tunneling](#quantumhottopics-tunneling)\n - [Quantum Entanglment](#quantumhottopics-entanglment)\n \t- [Quantum Eigen Spectrum](#quantumhottopics-eigenspectrum)\n - [Quantum Dots](#quantumhottopics-dots)\n - [Quantum elctro dynamics](#quantumhottopics-electrodynamics)\n - [Quantum teleportation](#quantumhottopics-teleportation)\n - [Quantum Supremacy](#quantumhottopics-supremacy)\n - [Quantum Zeno Effect](#quantumhottopics-zenoeffect)\n - [Quantum Cohomology](#quantumhottopics-cohomology)\n - [Quantum Chromodynamics](#quantumhottopics-chromodynamics)\n - [Quantum Darwinism](#quantumhottopics-darwinism)\n - [Quantum Coherence](#quantumhottopics-coherence)\n - [Quantum Decoherence](#quantumhottopics-decoherence) \n - [Topological Quantum Computing](#quantumhottopics-topologicalcomputing)\n - [Topological Quantum Field Theory](#quantumhottopics-topologicalfieldtheory)\n - [Quantum Knots](#quantumhottopics-knots)\n - [Topological Entanglment](#quantumhottopics-topologicalentanglment)\n - [Boson Sampling](#quantumhottopics-bosonsampling)\n - [Quantum Convolutional Code](#quantumhottopics-convolutionalcode)\n - [Stabilizer Code](#quantumhottopics-stabilizercode)\n - [Quantum Chaos](#quantumhottopics-chaos)\n - [Quantum Game Theory](#quantumhottopics-quantumgametheory)\n - [Quantum Channel](#quantumhottopics-quantumchannel)\n - [Tensor Space Theory](#quantumhottopics-tensorspacetheory)\n - [Quantum Leap](#quantumhottopics-quantumleap)\n - [Quantum Mechanics for Time Travel](#quantumhottopics-quantumtimetravel)\n - [Quantum Secured Block Chain](#quantumhottopics-quantumblockchain)\n - [Quantum Internet](#quantumhottopics-quantuminternet)\n - [Quantum Optical Network](#quantumhottopics-quantumopticalnetwork)\n - [Quantum Interference](#quantumhottopics-quantuminterference)\n - [Quantum Optical Network](#quantumhottopics-quantumopticalnetwork)\n - [Quantum Operating System](#quantumhottopics-quantumoperatingsystem)\n - [Electron Fractionalization](#quantumhottopics-electronfractionalization)\n \t- [Flip-Flop Quantum Computer](#quantumhottopics-flipflopquantumcomputer)\n - [Quantum Information with Gaussian States](#quantumhottopics-quantuminformationgaussianstates)\n - [Quantum Anomaly Detection](#quantumhottopics-quantumanomalydetection)\n \t- [Distributed Secure Quantum Machine Learning](#quantumhottopics-distributedsecureqml)\n - [Decentralized Quantum Machine Learning](#quantumhottopics-decentralizedqml)\n - [Artificial Agents for Quantum Designs](#quantumhottopics-artificialagents)\n - [Light Based Quantum Chips for AI Training](#quantumhottopics-quantumlightchipsai)\n- [QUANTUM STATE PREPARATION ALGORITHM FOR MACHINE LEARNING](#quantumstatepreparationalgorithm)\n - [Pure Quantum State](#quantumstatepreparationalgorithm-purequantumstate)\n - [Product State](#quantumstatepreparationalgorithm-productstate)\n - [Matrix Product State](#quantumstatepreparationalgorithm-matrixproductstate)\n - [Greenberger–Horne–Zeilinger State](#quantumstatepreparationalgorithm-Greenberger)\n - [W state](#quantumstatepreparationalgorithm-wstate)\n - [AKLT model](#quantumstatepreparationalgorithm-akltmodel)\n - [Majumdar–Ghosh Model](#quantumstatepreparationalgorithm-majumdarmodel)\n - [Multistate Landau–Zener Models](#quantumstatepreparationalgorithm-Landauzenermodels)\n - [Projected entangled-pair States](#quantumstatepreparationalgorithm-peps)\n - [Infinite Projected entangled-pair States](#quantumstatepreparationalgorithm-ipeps)\n - [Corner Transfer Matrix Method](#quantumstatepreparationalgorithm-cornertransfermatrix)\n - [Tensor-entanglement Renormalization](#quantumstatepreparationalgorithm-tensorentanglerenormaization)\n - [Tree Tensor Network for Supervised Learning](#quantumstatepreparationalgorithm-treetensornetwork)\n- [QUANTUM MACHINE LEARNING VS DEEP LEARNING](#qmlvsdl)\n- [QUANTUM MEETUPS](#quantummeetups)\n- [QUANTUM GOOGLE GROUPS](#quantumgroups)\n- [QUANTUM BASED COMPANIES](#quantumcompanies)\n- [QUANTUM LINKEDLIN](#quantumlinkedlin)\n- [QUANTUM BASED DEGREES](#quantumdegrees)\n- [CONSOLIDATED QUANTUM ML BOOKS](#quantumconsolidatedbooks)\n- [CONSOLIDATED QUANTUM ML VIDEOS](#quantumconsolidatedvideos)\n- [CONSOLIDATED QUANTUM ML Reserach Papers](#quantumconsolidatedresearchpapers)\n- [CONSOLIDATED QUANTUM ML Reserach Scientist](#quantumconsolidatedresearchscientist)\n- [RECENT QUANTUM UPDATES FORUM ,PAGES AND NEWSLETTER](#quantumforumsnewsletter)\n \n\n\u003C!-- \u002FMarkdownTOC -->\n\n\u003Ca name=\"introduction\">\u003C\u002Fa>\n## INTRODUCTION\n\n\u003Ca name=\"introduction-why-quantum-machine-learning\">\u003C\u002Fa>\n#### Why Quantum Machine Learning?\n \n##### Machine Learning(ML) is just a term in recent days but the work effort start from 18th century.\n\n##### What is Machine Learning ? , In Simple word the answer is making the computer or application to learn themselves . So its totally related with computing fields like computer science and IT ? ,The answer is not true . ML is a common platform which is mingled in all the aspects of the life from agriculture to mechanics . Computing is a key component to use ML easily and effectively . To be more clear ,Who is the mother of ML ?, As no option Mathematics is the mother of ML . The world tremendous invention complex numbers given birth to this field . Applying mathematics to the real life problem always gives a solution . From Neural Network to the complex DNA is running under some specific mathematical formulas and theorems.\n\n##### As computing technology growing faster and faster mathematics entered into this field and makes the solution via computing to the real world . In the computing technology timeline once a certain achievements reached peoples interested to use advanced mathematical ideas such as complex numbers ,eigen etc and its the kick start for the ML field such as Artificial Neural Network ,DNA Computing etc.\n\n##### Now the main question, why this field is getting boomed now a days ? , From the business perspective , 8-10 Years before during the kick start time for ML ,the big barrier is to merge mathematics into computing field . people knows well in computing has no idea on mathematics and research mathematician has no idea on what is computing . The education as well as the Job Opportunities is like that in that time . Even if a person tried to study both then the business value for making a product be not good.\n\n##### Then the top product companies like Google ,IBM ,Microsoft decided to form a team with mathematician ,a physician and a computer science person to come up with various ideas in this field . Success of this team made some wonderful products and they started by providing cloud services using this product . Now we are in this [stage](https:\u002F\u002Fcloud.google.com\u002Fvision\u002F).\n\n##### So what's next ? , As mathematics reached the level of time travel concepts but the computing is still running under classical mechanics . the companies understood, the computing field must have a change from classical to quantum, and they started working on the big Quantum computing field, and the market named this field as Quantum Information Science .The kick start is from Google and IBM with the Quantum Computing processor (D-Wave) for making Quantum Neural Network .The field of Quantum Computer Science and Quantum Information Science will do a big change in AI in the next 10 years. Waiting to see that........... .([google](https:\u002F\u002Fresearch.google.com\u002Fpubs\u002FQuantumAI.html), [ibm](http:\u002F\u002Fresearch.ibm.com\u002Fibm-q\u002F)).\n\n##### References\n \n* [D-Wave](https:\u002F\u002Fwww.dwavesys.com\u002Fquantum-computing) - Owner of a quantum processor\n* [Google](https:\u002F\u002Fresearch.google.com\u002Fpubs\u002FQuantumAI.html) - Quantum AI Lab\n* [IBM](http:\u002F\u002Fresearch.ibm.com\u002Fibm-q\u002F) - Quantum Computer Lab\n* [Quora](https:\u002F\u002Fwww.quora.com\u002FIs-quantum-computing-the-future-of-AI) - Question Regarding future of quantum AI\n* [NASA](https:\u002F\u002Fti.arc.nasa.gov\u002Ftech\u002Fdash\u002Fphysics\u002Fquail\u002F) - NASA Quantum Works\n* [Youtube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=CMdHDHEuOUE) - Google Video of a Quantum Processor\n* [external-link](http:\u002F\u002Fwww.huffingtonpost.com\u002F2013\u002F07\u002F29\u002Fquantum-computers-ai-artificial-intelligence-studies_n_3664011.html) - MIT Review\n* [microsoft new product](https:\u002F\u002Fwww.microsoft.com\u002Fen-us\u002Fquantum) - Newly Launched Microsoft Quantum Language and Development Kit\n* [microsoft](https:\u002F\u002Fwww.microsoft.com\u002Fen-us\u002Fresearch\u002Fproject\u002Flanguage-integrated-quantum-operations-liqui\u002F) - Microsoft Quantum Related Works\n* [Google2](https:\u002F\u002Fresearch.googleblog.com\u002F2009\u002F12\u002Fmachine-learning-with-quantum.html) - Google Quantum Machine Learning Blog\n* [BBC](http:\u002F\u002Fwww.bbc.co.uk\u002Fprogrammes\u002Fp052800h) - About Google Quantum Supremacy,IBM Quantum Computer and Microsoft Q\n* [Google Quantum Supremacy](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=-ZNEzzDcllU) - Latest 2019 Google Quantum Supremacy Achievement\n* [IBM Quantum Supremacy](https:\u002F\u002Fwww.ibm.com\u002Fblogs\u002Fresearch\u002F2019\u002F10\u002Fon-quantum-supremacy\u002F) - IBM Talk on Quantum Supremacy as a Primer\n* [VICE on the fight](https:\u002F\u002Fwww.vice.com\u002Fen_in\u002Farticle\u002Fvb5jxd\u002Fwhy-ibm-thinks-google-hasnt-achieved-quantum-supremacy) - IBM Message on Google Quantum Supremacy\n* [IBM Zurich Quantum Safe Cryptography](https:\u002F\u002Fwww.zurich.ibm.com\u002Fsecurityprivacy\u002Fquantumsafecryptography.html) - An interesting startup to replace all our Certificate Authority Via Cloud and IBM Q\n\n\u003Ca name=\"basics\">\u003C\u002Fa>\n## BASICS\n\n\u003Ca name=\"basics-what-quantum-mechanics\">\u003C\u002Fa>\n#### What is Quantum Mechanics?\n \n##### In a single line study of an electron moved out of the atom then its classical mechanic ,vibrates inside the atom its quantum mechanics\n\n* [WIKIPEDIA](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FQuantum_mechanics) - Basic History and outline\n* [LIVESCIENCE](http:\u002F\u002Fwww.livescience.com\u002F33816-quantum-mechanics-explanation.html). - A survey\n* [YOUTUBE](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=7u_UQG1La1o) - Simple Animation Video Explanining Great.\n\n\u003Ca name=\"basics-what-quantum-computing\">\u003C\u002Fa>\n#### What is Quantum Computing? \n \n##### A way of parallel execution of multiple processess in a same time using qubit ,It reduces the computation time and size of the processor probably in neuro size \n\n* [WIKIPEDIA](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FQuantum_computing) - Basic History and outline\n* [WEBOPEDIA](http:\u002F\u002Fwww.webopedia.com\u002FTERM\u002FQ\u002Fquantum_computing.html). - A survey\n* [YOUTUBE](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=g_IaVepNDT4) - Simple Animation Video Explanining Great.\n\n\u003Ca name=\"basics-quantum-classical-vs\">\u003C\u002Fa>\n#### Quantum Computing vs Classical Computing\n \n* [LINK](http:\u002F\u002Fwww.thphys.nuim.ie\u002Fstaff\u002Fjoost\u002FTQM\u002FQvC.html) - Basic outline\n\n \n\u003Ca name=\"quantumcomputing\">\u003C\u002Fa>\n## Quantum Computing\n\n\u003Ca name=\"quantumcomputing-atom-structure\">\u003C\u002Fa>\n#### Atom Structure\n \n##### one line : Electron Orbiting around the nucleous in an eliptical format\n \n* [YOUTUBE](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=g_IaVepNDT4) - A nice animation video about the basic atom structure \n\n[![atom](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fkrishnakumarsekar_awesome-quantum-machine-learning_readme_db1bb139523e.gif)](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FAtom)\n \n\u003Ca name=\"quantumcomputing-photon-wave\">\u003C\u002Fa>\n#### Photon Wave\n \n##### one line : Light nornmally called as wave transmitted as photons as similar as atoms in solid particles\n \n* [YOUTUBE](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=fwXQjRBLwsQ) - A nice animation video about the basic photon 1 \n* [YOUTUBE](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=KKr91v7yLcM) - A nice animation video about the basic photon 2\n \n[![Photon wave](https:\u002F\u002Fwww.wired.com\u002Fimages_blogs\u002Fwiredscience\u002F2013\u002F07\u002Fphoton1.jpg)](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FPhoton)\n \n\u003Ca name=\"quantumcomputing-elecfluctuation-spin\">\u003C\u002Fa>\n#### Electron Fluctuation or spin\n \n##### one line : When a laser light collide with solid particles the electrons of the atom will get spin between the orbitary layers of the atom\n\n[![Spin](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fkrishnakumarsekar_awesome-quantum-machine-learning_readme_cccb4a13ac40.gif)](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FSpin_(physics))\n \n* [YOUTUBE](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=J3xLuZNKhlY) - A nice animation video about the basic Electron Spin 1 \n* [YOUTUBE](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=3k5IWlVdMbo) - A nice animation video about the basic Electron Spin 2\n* [YOUTUBE](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=jvvkomcmyuo) - A nice animation video about the basic Electron Spin 3\n \n\u003Ca name=\"quantumcomputing-states\">\u003C\u002Fa>\n#### States\n \n##### one line : Put a point on the spinning electron ,if the point is in the top then state 1 and its in bottom state 0 \n\n[![States](https:\u002F\u002F3c1703fe8d.site.internapcdn.net\u002Fnewman\u002Fgfx\u002Fnews\u002Fhires\u002F2016\u002Fadeeplookint.jpg)](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FQuantum_state)\n \n* [YOUTUBE](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=sICXOwOwS4E) - A nice animation video about the Quantum States\n \n\u003Ca name=\"quantumcomputing-superposition\">\u003C\u002Fa>\n#### SuperPosition\n \n##### two line : During the spin of the electron the point may be in the middle of upper and lower position, So an effective decision needs to take on the point location either 0 or 1 . Better option to analyse it along with other electrons using probability and is called superposition\n\n[![SuperPosition](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fkrishnakumarsekar_awesome-quantum-machine-learning_readme_cb4f7ca4c3aa.gif)](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FQuantum_superposition)\n \n* [YOUTUBE](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=hkmoZ8e5Qn0) - A nice animation video about the Quantum Superposition\n\n\u003Ca name=\"quantumcomputing-superpostion-machinelearning\">\u003C\u002Fa>\n#### SuperPosition specific for machine learning(Quantum Walks)\n \n##### one line : As due to computational complexity ,quantum computing only consider superposition between limited electrons ,In case to merge more than one set quantum walk be the idea\n\n[![SuperPosition specific for machine learning](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fkrishnakumarsekar_awesome-quantum-machine-learning_readme_a53661c1d524.gif)](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FQuantum_walk)\n \n* [YOUTUBE](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=86QsYPxoBow) - A nice video about the Quantum Walks\n \n\u003Ca name=\"quantumcomputing-classicalbit\">\u003C\u002Fa>\n#### Classical Bits\n \n##### one line : If electron moved from one one atom to other ,from ground state to excited state a bit value 1 is used else bit value 0 used\n\n[![Classical Bits](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fkrishnakumarsekar_awesome-quantum-machine-learning_readme_d5ea90517510.gif)](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FBit)\n \n\u003Ca name=\"quantumcomputing-qubit\">\u003C\u002Fa>\n#### Qubit\n \n##### one line : The superposition value of states of a set of electrons is Qubit \n\n[![Qubit](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fkrishnakumarsekar_awesome-quantum-machine-learning_readme_5b774b87db5b.png)](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FQubit)\n \n* [YOUTUBE](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=zNzzGgr2mhk) - A nice video about the Quantum Bits 1\n* [YOUTUBE](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=F8U1d2Hqark&t=179s) - A nice video about the Bits and Qubits 2\n \n\u003Ca name=\"quantumcomputing-basicgates\">\u003C\u002Fa>\n#### Basic Gates in Quantum Computing\n \n##### one line : As like NOT, OR and AND , Basic Gates like NOT, Hadamard gate , SWAP, Phase shift etc can be made with quantum gates \n\n[![Basic Gates in Quantum Computing](http:\u002F\u002Fwww.mdpi.com\u002Fentropy\u002Fentropy-16-05290\u002Farticle_deploy\u002Fhtml\u002Fimages\u002Fentropy-16-05290f1-1024.png)](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FQuantum_gate)\n \n* [YOUTUBE](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=2Qsh_w2kq9Y) - A nice video about the Quantum Gates\n \n\u003Ca name=\"quantumcomputing-diode\">\u003C\u002Fa>\n#### Quantum Diode\n \n##### one line : Quantum Diodes using a different idea from normal diode, A bunch of laser photons trigger the electron to spin and the quantum magnetic flux will capture the information \n\n[![Diodes in Quantum Computing1](https:\u002F\u002Fwww.ifw-dresden.de\u002Fuserfiles\u002Fgroups\u002Fiin_folder\u002Fresearch\u002Fphotonics\u002Fpicture_laser_iin.jpg)](http:\u002F\u002Fonlinelibrary.wiley.com\u002Fdoi\u002F10.1002\u002Fadma.201200537\u002Fabstract)\n[![Diodes in Quantum Computing2](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fkrishnakumarsekar_awesome-quantum-machine-learning_readme_ae452ae2e4ad.jpg)](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fncomms12978)\n[![Diodes in Quantum Computing3](https:\u002F\u002F3c1703fe8d.site.internapcdn.net\u002Fnewman\u002Fgfx\u002Fnews\u002Fhires\u002F2013\u002Fnanoscaleeng.jpg)](https:\u002F\u002Fphys.org\u002Fnews\u002F2013-10-nanoscale-boosts-quantum-dot-emitting.html)\n\n \n* [YOUTUBE](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=doyK1olswX4) - A nice video about the Quantum Diode\n \n\u003Ca name=\"quantumcomputing-transistor\">\u003C\u002Fa>\n#### Quantum Transistors\n \n##### one line : A transistor default have Source ,drain and gate ,Here source is photon wave ,drain is flux and gate is classical to quantum bits \n\n[![Quantum Transistors1](https:\u002F\u002Fimages.sciencedaily.com\u002F2010\u002F05\u002F100514075106_1_900x600.jpg)](http:\u002F\u002Fwww.mpq.mpg.de\u002F4987844\u002Fqip)\n[![Quantum Transistors2](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fkrishnakumarsekar_awesome-quantum-machine-learning_readme_2eb4740f91c9.png)](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FMagnetic_flux_quantum)\n\n \n* [QUORA](https:\u002F\u002Fwww.quora.com\u002FWhat-is-the-equivalent-of-the-transistor-in-a-quantum-computer) -Discussion about the Quantum Transistor\n* [YOUTUBE](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=ZTxR2n2mvjc) - Well Explained\n \n\u003Ca name=\"quantumcomputing-processor\">\u003C\u002Fa>\n#### Quantum Processor\n \n##### one line : A nano integration circuit performing the quantum gates operation sorrounded by cooling units to reduce the tremendous amount of heat \n\n[![Quantum Processor1](https:\u002F\u002Fwww.dwavesys.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fcooling-082015%20copy.png)](https:\u002F\u002Fwww.dwavesys.com\u002Ftutorials\u002Fbackground-reading-series\u002Fintroduction-d-wave-quantum-hardware#h2-0)\n[![Quantum Processor2](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fkrishnakumarsekar_awesome-quantum-machine-learning_readme_7855ddc7a47c.png)](https:\u002F\u002Fquantumexperience.ng.bluemix.net\u002Fqstage\u002F?cm_mc_uid=36641337812614766932472&cm_mc_sid_50200000=1493295650#\u002Fuser-guide)\n[![Quantum Processor3](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fkrishnakumarsekar_awesome-quantum-machine-learning_readme_bb40f49a6b99.jpg)](https:\u002F\u002Fwww.cbinsights.com\u002Fblog\u002Fquantum-computing-corporations-list\u002F)\n \n* [YOUTUBE](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=CMdHDHEuOUE) - Well Explained\n \n\u003Ca name=\"quantumcomputing-qram\">\u003C\u002Fa>\n#### Quantum Registery QRAM\n \n##### one line : Comapring the normal ram ,its ultrafast and very small in size ,the address location can be access using qubits superposition value ,for a very large memory set coherent superposition(address of address) be used\n\n[![QRAM1](https:\u002F\u002Fai2-s2-public.s3.amazonaws.com\u002Ffigures\u002F2016-11-08\u002F94d487c6ef4d0fa5594eff352aac19e2bfd47ffa\u002F2-Figure1-1.png)](https:\u002F\u002Farxiv.org\u002Fpdf\u002F0708.1879.pdf)\n[![QRAM2](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fkrishnakumarsekar_awesome-quantum-machine-learning_readme_f704793f04e3.png)](https:\u002F\u002Fwww.researchgate.net\u002Fpublication\u002F51394884_Quantum_Random_Access_Memory)\n \n* [PDF](https:\u002F\u002Farxiv.org\u002Fpdf\u002F0807.4994.pdf) - very Well Explained\n\n\n\u003Ca name=\"qcmlbridge\">\u003C\u002Fa>\n## QUANTUM COMPUTING MACHINE LEARNING BRIDGE\n \n\n\u003Ca name=\"qcmlbridge-complexNumbers\">\u003C\u002Fa>\n#### Complex Numbers\n \n##### one line : Normally Waves Interference is in n dimensional structure , to find a polynomial equation n order curves ,better option is complex number \n\n[![Complex Numbers1](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fkrishnakumarsekar_awesome-quantum-machine-learning_readme_2d232779e745.gif)](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FComplex_number)\n[![Complex Numbers2](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fkrishnakumarsekar_awesome-quantum-machine-learning_readme_c1057a795508.png)](https:\u002F\u002Fwww.mathsisfun.com\u002Fnumbers\u002Fcomplex-numbers.html)\n[![Complex Numbers3](http:\u002F\u002Fwww.mathwarehouse.com\u002Falgebra\u002Fcomplex-number\u002Fimages\u002Fgraphs\u002Fdiagram-complex-plane.png)](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FComplex_number)\n \n* [YOUTUBE](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=T647CGsuOVU) - Wonderful Series very super Explained \n\n\u003Ca name=\"qcmlbridge-tensors\">\u003C\u002Fa>\n#### Tensors\n \n##### one line : Vectors have a direction in 2D vector space ,If on a n dimensional vector space ,vectors direction can be specify with the tensor ,The best solution to find the superposition of a n vector electrons spin space is representing vectors as tensors and doing tensor calculus\n\n[![Tensors1](https:\u002F\u002Fi.stack.imgur.com\u002F5QsMD.png)](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FTensor)\n[![Tensors2](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fkrishnakumarsekar_awesome-quantum-machine-learning_readme_9e764d96aa6f.jpg)](https:\u002F\u002Fwww.quantiki.org\u002Fwiki\u002Ftensor-product)\n[![Tensors3](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fkrishnakumarsekar_awesome-quantum-machine-learning_readme_2d4eb2aca44e.png)](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FTensor_product)\n[![Tensors4](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fkrishnakumarsekar_awesome-quantum-machine-learning_readme_2d4eb2aca44e.png)](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FTensor_product_of_Hilbert_spaces)\n \n* [YOUTUBE](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=f5liqUk0ZTw) - Wonderful super Explained tensors basics\n* [YOUTUBE](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=xzG6c96PsLs) - Quantum tensors basics \n \n\u003Ca name=\"qcmlbridge-tensors-network\">\u003C\u002Fa>\n#### Tensors Network\n \n##### one line : As like connecting multiple vectors ,multple tensors form a network ,solving such a network reduce the complexity of processing qubits\n\n[![Tensors Network1](http:\u002F\u002Fwww.cse.unsw.edu.au\u002F~billw\u002Fcs9444\u002Ftensor-stuff\u002Ftensor-intro-0418.gif)](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1306.2164.pdf)\n[![Tensors Network2](http:\u002F\u002Fwww.quantuminfo.physik.rwth-aachen.de\u002Fglobal\u002Fshow_picture.asp?id=aaaaaaaaaaiejix)](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FTensor_network_theory)\n[![Tensors Network3](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fkrishnakumarsekar_awesome-quantum-machine-learning_readme_2d4eb2aca44e.png)](https:\u002F\u002Fwww2.warwick.ac.uk\u002Ffac\u002Fsci\u002Fphysics\u002Fcurrent\u002Fpostgraduate\u002Fpglist\u002Fphrfbk\u002Fpresentations\u002Fleeds14.pdf)\n \n* [YOUTUBE](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=bD-CWgbsCeI&list=PLgKuh-lKre10UQnP7gBCFoKgq5KWIA7el) - Tensors Network Some ideas specifically for quantum algorithms\n \n\n\u003Ca name=\"quantumalgorithmsml\">\u003C\u002Fa>\n## QUANTUM MACHINE LEARNING ALGORITHMS\n \n\n\u003Ca name=\"quantumalgorithmsml-qknn\">\u003C\u002Fa>\n#### Quantum K-Nearest Neighbour\n \n##### info : Here the centroid(euclidean distance) can be detected using the swap gates test between two states of the qubit , As KNN is regerssive loss can be tally using the average \n \n* [PDF1 from Microsoft](https:\u002F\u002Fwww.microsoft.com\u002Fen-us\u002Fresearch\u002Fpublication\u002Fquantum-nearest-neighbor-algorithms-for-machine-learning\u002F) - Theory Explanation\n* [PDF2](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1409.3097.pdf) - A Good Material to understand the basics\n* [Matlab](https:\u002F\u002Fgithub.com\u002Fkrishnakumarsekar\u002F) - Yet to come soon\n* [Python](https:\u002F\u002Fgithub.com\u002Fkrishnakumarsekar\u002F) - Yet to come soon\n \n\u003Ca name=\"quantumalgorithmsml-kmeans\">\u003C\u002Fa>\n#### Quantum K-Means\n \n##### info : Two Approaches possible ,1. FFT and iFFT to make an oracle and calculate the means of superposition 2. Adiobtic Hamiltonian generation and solve the hamiltonian to determine the cluster \n \n* [PDF1](https:\u002F\u002Fpdfs.semanticscholar.org\u002F6d77\u002F54d33958b4a41d57ec99558eb28ae88f9884.pdf) - Applying Quantum Kmeans on Images in a nice way\n* [PDF2](http:\u002F\u002Fwww.machinelearning.org\u002Fproceedings\u002Ficml2007\u002Fpapers\u002F518.pdf) - Theory\n* [PDF3](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1307.0411.pdf) - Explaining well the K-means clustering using hamiltonian \n* [Matlab](https:\u002F\u002Fgithub.com\u002Fkrishnakumarsekar\u002F) - Yet to come soon\n* [Python](https:\u002F\u002Fgithub.com\u002Fkrishnakumarsekar\u002F) - Yet to come soon\n \n\u003Ca name=\"quantumalgorithmsml-qfcm\">\u003C\u002Fa>\n#### Quantum Fuzzy C-Means\n \n##### info : As similar to kmeans fcm also using the oracle dialect ,but instead of means,here oracle optimization followed by a rotation gate is giving a good result\n \n* [PDF1](https:\u002F\u002Fpdfs.semanticscholar.org\u002F6d77\u002F54d33958b4a41d57ec99558eb28ae88f9884.pdf) - Theory\n* [Matlab](https:\u002F\u002Fgithub.com\u002Fkrishnakumarsekar\u002F) - Yet to come soon\n* [Python](https:\u002F\u002Fgithub.com\u002Fkrishnakumarsekar\u002F) - Yet to come soon\n \n\u003Ca name=\"quantumalgorithmsml-svm\">\u003C\u002Fa>\n#### Quantum Support Vector Machine\n \n##### info : A little different from above as here kernel preparation is via classical and the whole training be in oracles and oracle will do the classification, As SVM is linear ,An optimal Error(Optimum of the Least Squares Dual Formulation) Based regression is needed to improve the performance\n \n* [PDF1](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1307.0471.pdf) - Nice Explanation but little hard to understand :)\n* [PDF2](http:\u002F\u002Fwww.scirp.org\u002Fjournal\u002FPaperInformation.aspx?paperID=72542) - Nice Application of QSVM\n* [Matlab](https:\u002F\u002Fgithub.com\u002Fkrishnakumarsekar\u002F) - Yet to come soon\n* [Python](https:\u002F\u002Fgithub.com\u002Fkrishnakumarsekar\u002F) - Yet to come soon\n \n\u003Ca name=\"quantumalgorithmsml-genetic\">\u003C\u002Fa>\n#### Quantum Genetic Algorithm\n \n##### info : One of the best algorithm suited for Quantum Field ,Here the chromosomes act as qubit vectors ,the crossover part carrying by an evaluation and the mutation part carrying by the rotation of gates\n \n[![Flow Chart](https:\u002F\u002Fwww.hindawi.com\u002Fjournals\u002Fmpe\u002F2013\u002F730749.fig.001.jpg)]() \n\n* [PDF1](https:\u002F\u002Fwww.hindawi.com\u002Fjournals\u002Fmpe\u002F2013\u002F730749\u002F) - Very Beautiful Article , well explained and superp \n* [PDF2](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1202.2026.pdf) - A big theory :)\n* [PDF3](http:\u002F\u002Fccis2k.org\u002Fiajit\u002FPDF\u002Fvol.9,no.3\u002F2107-6.pdf) - Super Comparison\n* [Matlab](http:\u002F\u002Fwww.codelooker.com\u002Fid\u002F155\u002F717734.html) - Simulation\n* [Python1](https:\u002F\u002Fgithub.com\u002FResearchCodesHub\u002FQuantumGeneticAlgorithms\u002F) - Simulation\n* [Python2](https:\u002F\u002Fgithub.com\u002Fkrishnakumarsekar\u002F) - Yet to come\n \n\u003Ca name=\"quantumalgorithmsml-hmm\">\u003C\u002Fa>\n#### Quantum Hidden Morkov Models\n \n##### info : As HMM is already state based ,Here the quantum states acts as normal for the markov chain and the shift between states is using quantum operation based on probability distribution \n \n[![Flow Chart](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fkrishnakumarsekar_awesome-quantum-machine-learning_readme_bce12b68560b.png)]() \n\n* [PDF1](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1503.08760.pdf) - Nice idea and explanation \n* [PDF2](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1207.4304.pdf) - Nice but a different concept little\n* [Matlab](https:\u002F\u002Fgithub.com\u002Fkrishnakumarsekar\u002F) - Yet to come\n* [Python1](https:\u002F\u002Fgithub.com\u002Fkrishnakumarsekar\u002F) - Yet to come\n* [Python2](https:\u002F\u002Fgithub.com\u002Fkrishnakumarsekar\u002F) - Yet to come\n \n\u003Ca name=\"quantumalgorithmsml-bayesian\">\u003C\u002Fa>\n#### Quantum state classification with Bayesian methods\n \n##### info : Quantum Bayesian Network having the same states concept using quantum states,But here the states classification to make the training data as reusable is based on the density of the states(Interference) \n \n[![Bayesian Network Sample1](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fkrishnakumarsekar_awesome-quantum-machine-learning_readme_a51210f223ad.jpg)]() \n[![Bayesian Network Sample2](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fkrishnakumarsekar_awesome-quantum-machine-learning_readme_9987a6dcadf2.jpg)]() \n[![Bayesian Network Sample3](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fkrishnakumarsekar_awesome-quantum-machine-learning_readme_0fb5f46316ce.jpg)]() \n \n* [PDF1](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1204.1550.pdf) - Good Theory \n* [PDF2](https:\u002F\u002Fwww.ncbi.nlm.nih.gov\u002Fpmc\u002Farticles\u002FPMC4726808\u002F) - Good Explanation\n* [Matlab](https:\u002F\u002Fgithub.com\u002Fkrishnakumarsekar\u002F) - Yet to come\n* [Python1](https:\u002F\u002Fgithub.com\u002Fkrishnakumarsekar\u002F) - Yet to come\n* [Python2](https:\u002F\u002Fgithub.com\u002Fkrishnakumarsekar\u002F) - Yet to come\n \n\u003Ca name=\"quantumalgorithmsml-antcolony\">\u003C\u002Fa>\n#### Quantum Ant Colony Optimization\n \n##### info : A good algorithm to process multi dimensional equations, ACO is best suited for Sales man issue , QACO is best suited for Sales man in three or more dimension, Here the quantum rotation circuit is doing the peromene update and qubits based colony communicating all around the colony in complex space\n \n[![Ant Colony Optimization 1](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fkrishnakumarsekar_awesome-quantum-machine-learning_readme_61e21d840730.jpg)]() \n \n* [PDF1](http:\u002F\u002Fac.els-cdn.com\u002FS2212667812001359\u002F1-s2.0-S2212667812001359-main.pdf?_tid=42e0cd66-2f4a-11e7-920f-00000aacb361&acdnat=1493738345_8f536599e404c7588811ddd49c484688) - Good Concept \n* [PDF2](http:\u002F\u002Fwww.sersc.org\u002Fjournals\u002FIJMUE\u002Fvol10_no11_2015\u002F19.pdf) - Good Application\n* [Matlab](https:\u002F\u002Fgithub.com\u002Fkrishnakumarsekar\u002F) - Yet to come\n* [Python1](https:\u002F\u002Fgithub.com\u002Fkrishnakumarsekar\u002F) - Yet to come\n* [Python2](https:\u002F\u002Fgithub.com\u002Fkrishnakumarsekar\u002F) - Yet to come\n \n\u003Ca name=\"quantumalgorithmsml-caautomata\">\u003C\u002Fa>\n#### Quantum Cellular Automata\n \n##### info : One of the very complex algorithm with various types specifically used for polynomial equations and to design the optimistic gates for a problem, Here the lattice is formed using the quatum states and time calculation is based on the change of the state between two qubits ,Best suited for nano electronics\n \n[![Quantum Cellular Automata](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fkrishnakumarsekar_awesome-quantum-machine-learning_readme_fe6b66185175.jpg)]() \n \n* [Wikipedia](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FQuantum_cellular_automaton) - Basic \n* [PDF1](https:\u002F\u002Farxiv.org\u002Fpdf\u002F0808.0679.pdf) - Just to get the keywords\n* [PDF2](http:\u002F\u002Fieee-hpec.org\u002F2013\u002Findex_htm_files\u002F7-Improved-Eigensolver-Baldwin-2867489.pdf) - Nice Explanation and an easily understandable application \n* [Matlab](https:\u002F\u002Fgithub.com\u002Fkrishnakumarsekar\u002F) - Yet to come\n* [Python1](https:\u002F\u002Fgithub.com\u002Fkrishnakumarsekar\u002F) - Yet to come\n* [Python2](https:\u002F\u002Fgithub.com\u002Fkrishnakumarsekar\u002F) - Yet to come\n \n \n\u003Ca name=\"qnn\">\u003C\u002Fa>\n## QAUNTUM NEURAL NETWORK\n \n[![QNN 1](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fkrishnakumarsekar_awesome-quantum-machine-learning_readme_072044ecf6ad.png)](https:\u002F\u002Fsilky.github.io\u002Fposts\u002F2016-12-11-quantum-neural-networks.html)\n\n##### one line : Its really one of the hardest topic , To understand easily ,Normal Neural Network is doing parallel procss ,QNN is doing parallel of parallel processess ,In theory combination of various activation functions is possible in QNN ,In Normal NN more than one activation function reduce the performance and increase the complexity\n\n\u003Ca name=\"qnn-perceptron\">\u003C\u002Fa>\n#### Quantum perceptrons\n \n##### info : Perceptron(layer) is the basic unit in Neural Network ,The quantum version of perceptron must satisfy both linear and non linear problems , Quantum Concepts is combination of linear(calculus of superposition) and nonlinear(State approximation using probability) ,To make a perceptron in quantum world ,Transformation(activation function) of non linearity to certain limit is needed ,which is carrying by phase estimation algorithm\n \n[![Quantum Perceptron 1](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fkrishnakumarsekar_awesome-quantum-machine-learning_readme_32c00ed15e82.jpg)](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FActivation_function) \n[![Quantum Perceptron 2](https:\u002F\u002Fwww.nature.com\u002Farticle-assets\u002Fnpg\u002Fsrep\u002F2014\u002F140107\u002Fsrep03589\u002Fimages\u002Fm685\u002Fsrep03589-f2.jpg)](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FQuantum_phase_estimation_algorithm) \n[![Quantum Perceptron 3](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fkrishnakumarsekar_awesome-quantum-machine-learning_readme_250143ef0fc7.png)]()\n[![Quantum Perceptron 4](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fkrishnakumarsekar_awesome-quantum-machine-learning_readme_79cf8276018d.gif)](https:\u002F\u002Fwww.omicsonline.org\u002Fopen-access\u002Fquantum-neural-network-based-parts-of-speech-tagger-for-hindi-0976-4860-5-137-152.pdf.php?aid=35658) \n[![Quantum Perceptron 5](https:\u002F\u002F3c1703fe8d.site.internapcdn.net\u002Fnewman\u002Fcsz\u002Fnews\u002F800\u002F2015\u002Fneuralqubits.jpg)](https:\u002F\u002Fwww.researchgate.net\u002Fpublication\u002F231178445_Quantum_Learning_and_Quantum_Perceptrons) \n\n* [PDF1](https:\u002F\u002Farxiv.org\u002Fpdf\u002Fquant-ph\u002F0201144.pdf) - Good Theory \n* [PDF2](http:\u002F\u002Faxon.cs.byu.edu\u002Fpapers\u002Fricks.nips03.pdf\u002F) - Good Explanation\n* [Matlab](https:\u002F\u002Fgithub.com\u002Fkrishnakumarsekar\u002F) - Yet to come\n* [Python1](https:\u002F\u002Fgithub.com\u002Fkrishnakumarsekar\u002F) - Yet to come\n* [Python2](https:\u002F\u002Fgithub.com\u002Fkrishnakumarsekar\u002F) - Yet to come\n \n\u003Ca name=\"quantumstatistics\">\u003C\u002Fa>\n## QAUNTUM STATISTICAL DATA ANALYSIS\n \n[![quantumstatistics1](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fkrishnakumarsekar_awesome-quantum-machine-learning_readme_37703ac8db62.jpg)](https:\u002F\u002Fwww.slideshare.net\u002Ftanafuyu\u002Fslide-2014-rims1031public)\n[![quantumstatistics2](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fkrishnakumarsekar_awesome-quantum-machine-learning_readme_464870c6b232.jpg)](https:\u002F\u002Fwww.slideshare.net\u002Ftanafuyu\u002Fslide-2014-rims1031public)\n[![quantumstatistics3](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fkrishnakumarsekar_awesome-quantum-machine-learning_readme_b3a2d1350af2.jpg)](https:\u002F\u002Fwww.slideshare.net\u002Ftanafuyu\u002Fslide-2014-rims1031public)\n[![quantumstatistics4](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fkrishnakumarsekar_awesome-quantum-machine-learning_readme_0c564a029925.png)](https:\u002F\u002Farxiv.org\u002Fpdf\u002F0802.1296.pdf)\n[![quantumstatistics5](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fkrishnakumarsekar_awesome-quantum-machine-learning_readme_755d97d11f3b.jpg)](https:\u002F\u002Fwww.slideshare.net\u002Fmleifer\u002Fquantum-dynamics-as-generalized-conditional-probabilities)\n[![quantumstatistics6]( https:\u002F\u002Fimage.slidesharecdn.com\u002Fguelph200609-090408123316-phpapp02\u002F95\u002Fquantum-dynamics-as-generalized-conditional-probabilities-4-728.jpg?cb=1239194048)](https:\u002F\u002Fwww.slideshare.net\u002Fmleifer\u002Fconditional-density-operators-in-quantum-information) \n\n##### one line : An under research concept ,It can be seen in multiple ways, one best way if you want to apply n derivative for a problem in current classical theory its difficult to compute as its serialization problem instead if you do parallelization of differentiation you must estimate via probability the value in all flows ,Quantum Probability Helps to achieve this ,as the loss calculation is very less . the other way comparatively booming is Quantum Bayesianism, its a solution to solve most of the uncertainity problem in statistics to combine time and space in highly advanced physical research \n\n \n\u003Ca name=\"qpl\">\u003C\u002Fa>\n## QUANTUM PROGRAMMING LANGUAGES , TOOLs and SOFTWARES\n \n\n\u003Ca name=\"qpl-all\">\u003C\u002Fa>\n#### All\n \n##### info : All Programming languages ,softwares and tools in alphabetical order \n \n* [Software](https:\u002F\u002Fwww.quantiki.org\u002Fwiki\u002Flist-qc-simulators) - Nice content of all\n* [Python library](http:\u002F\u002Fqutip.org\u002F) - A python library\n* [Matlab based python library](https:\u002F\u002Fpypi.python.org\u002Fpypi\u002Fqit) - Matlab Python Library\n* [Quantum Tensor Network Github](https:\u002F\u002Fgithub.com\u002Femstoudenmire\u002FTNML) - Tensor Network\n* [Bayesforge](http:\u002F\u002Fbayesforge.com\u002F) - A Beautiful Amazon Web Service Enabled Framework for Quantum Alogorithms and Data Analytics\n* [Rigetti](https:\u002F\u002Fgithub.com\u002Frigetticomputing) - A best tools repository to use quantum computer in real time\n* [Rigetti Forest](http:\u002F\u002Fwww.rigetti.com\u002Findex.php\u002Fforest) - An API to connect Quantum Computer\n* [quil\u002FpyQuil](http:\u002F\u002Fpyquil.readthedocs.io\u002Fen\u002Flatest\u002Foverview.html) - A quantum instruction language to use forest framework\n* [Grove](https:\u002F\u002Fgithub.com\u002Frigetticomputing\u002Fgrove) - Grove is a repository to showcase quantum Fourier transform, phase estimation, the quantum approximate optimization algorithm, and others developed using Forest\n* [QISKit](https:\u002F\u002Fgithub.com\u002FQISKit) - A IBM Kit to access quantum computer and mainly for quantum circuits\n* [IBM Bluemix Simulator](https:\u002F\u002Fquantumexperience.ng.bluemix.net\u002Fqx\u002Feditor) - A Bluemix Simulator for Quantum Circuits\n* [Microsoft Quantum Development Kit](https:\u002F\u002Fmarketplace.visualstudio.com\u002Fitems?itemName=quantum.DevKit) - Microsoft Visual Studio Enbaled Kit for Quantum Circuit Creation\n* [Microsoft \"Q#\"](https:\u002F\u002Fdocs.microsoft.com\u002Fen-us\u002Fquantum\u002Fquantum-WriteAQuantumProgram?view=qsharp-preview) - Microsoft Q Sharp a new Programming Language for Quantum Circuit Creation\n* [qiskit api python](https:\u002F\u002Fgithub.com\u002FQISKit\u002Fqiskit-api-py) - An API to connect IBM Quantum Computer ,With the generated token its easy to connect ,but very limited utils ,Lot of new utils will come soon \n* [Cyclops Tensor Framework](http:\u002F\u002Fsolomon2.web.engr.illinois.edu\u002Fctf\u002F) - A framework to do tensor network simulations\n* [Python ToolKit for chemistry and physics Quantum Algorithm simulations](https:\u002F\u002Fgithub.com\u002Fqmlcode\u002Fqml) - A New Started Project for simulating molecule and solids\n* [Bayesian Based Quatum Projects Repository](https:\u002F\u002Fgithub.com\u002Fartiste-qb-net) - A nice repository and the kickstarter of bayesforge\n* [Google Fermion Products](https:\u002F\u002Fgithub.com\u002Fquantumlib) - A newly launched product specifivally for chemistry simulation\n* [Tree Tensor Networks](https:\u002F\u002Fgithub.com\u002Fdingliu0305\u002FTree-Tensor-Networks-in-Machine-Learning) - Interesting Tensor Network in Incubator\n* [Deep Tensor Neural Network](https:\u002F\u002Fgithub.com\u002Fatomistic-machine-learning\u002Fdtnn) - Some useful information about Tensor Neural Network in Incubator\n* [Generative Tensorial Networks](http:\u002F\u002Fgtn.ai\u002F) - A startup to apply machine learning via tensor network for drug discovery\n* [Google Bristlecone](https:\u002F\u002Fresearch.googleblog.com\u002F2018\u002F03\u002Fa-preview-of-bristlecone-googles-new.html) - A new Quantum Processor from Google , Aimed for Future Hardwares with full fledged AI support\n* [XANADU](https:\u002F\u002Fwww.xanadu.ai\u002F) - A Light based Quantum Hardware(chips supports) and Software Company Started in Preparation Stage. Soon will be in market\n* [fathom computing](https:\u002F\u002Fwww.fathomcomputing.com\u002F) - A new concept to train the ai in a processor using light and quantum based concepts. soon products will be launch\n* [Alibaba Quantum Computing Cloud Service](https:\u002F\u002Fwww.alibabacloud.com\u002Fpress-room\u002Falibaba-cloud-and-cas-launch-one-of-the-worlds-most) - Cloud Service to access 11 Bit Quantum Computing Processor\n* [Atomistic Machine Learning Project](https:\u002F\u002Fgithub.com\u002Fatomistic-machine-learning) - Seems something Interesting with Deep Tensor Network for Quantum Chemistry Applications\n* [circQ and Google Works](https:\u002F\u002Fai.google\u002Fresearch\u002Fteams\u002Fapplied-science\u002Fquantum\u002F) - Google Top Efforts on Tools\n* [IBM Safe Cryptography on Cloud](https:\u002F\u002Fwww.sdxcentral.com\u002Farticles\u002Fnews\u002Fibm-drives-quantum-safe-cryptography-into-its-public-cloud\u002F2019\u002F08\u002F) - IBM Started and Developing a Quantm Safe Cryptography to replace all our Certificate Authority via Cloud\n* [Google Tensor Network Open Source](https:\u002F\u002Fai.googleblog.com\u002F2019\u002F06\u002Fintroducing-tensornetwork-open-source.html) - Google Started the Most Scientist Preferred Way To Use a Quantum Computer Circuit. Tensor Flow Which Makes Easy to Design the Network and Will Leave the Work Effect Of Gates, Processor Preparation and also going to tell the beauty of Maths\n* [Google Tensor Network Github](https:\u002F\u002Fgithub.com\u002Fgoogle\u002FTensorNetwork) - Github Project of Google Tensor Network\n* [Quantum Tensorflow](https:\u002F\u002Fgithub.com\u002Fkrishnakumarsekar\u002F) - Yet to come soon\n* [Quantum Spark](https:\u002F\u002Fgithub.com\u002Fkrishnakumarsekar\u002F) - Yet to come soon\n* [Quatum Map Reduce](https:\u002F\u002Fgithub.com\u002Fkrishnakumarsekar\u002F) - Yet to come soon\n* [Quantum Database](https:\u002F\u002Fgithub.com\u002Fkrishnakumarsekar\u002F) - Yet to come soon\n* [Quantum Server](https:\u002F\u002Fgithub.com\u002Fkrishnakumarsekar\u002F) - Yet to come soon\n* [Quantum Data Analytics](https:\u002F\u002Fgithub.com\u002Fkrishnakumarsekar\u002F) - Yet to come soon\n \n\n\u003Ca name=\"quantumhottopics\">\u003C\u002Fa>\n## QUANTUM HOT TOPICS\n \n\n\u003Ca name=\"quantumhottopics-deepquantumlearning\">\u003C\u002Fa>\n#### Deep Quantum Learning\n \n##### why and what is deep learning?\n###### In one line , If you know deep learning you can get a good job :) ,Even a different platform undergraduated and graduated person done a master specialization in deep learning can work in this big sector :), Practically speaking machine learning (vector mathematics) , deep learning (vector space(Graphics) mathematics) and big data are the terms created by big companies to make a trend in the market ,but in science and research there is no word such that , Now a days if you ask a junior person working in this big companies ,what is deep learning ,you will get some reply as \"doing linear regression with stochastic gradient for a unsupervised data using Convolutional Neural Network :)\" ,They knows the words clearly and knows how to do programming using that on a bunch of \"relative data\" , If you ask them about the FCM , SVM and HMM etc algorithms ,they will simply say these are olden days algorithms , deep learning replaced all :), But actually they dont know from the birth to the till level and the effectiveness of algorithms and mathematics ,How many mathematical theorems in vector, spaces , tensors etc solved to find this \"hiding the complexity technology\", They did not played with real non relative data like medical images, astro images , geology images etc , finding a relation and features is really complex and looping over n number of images to do pattern matching is a giant work , Now a days the items mentioned as deep learning (= multiple hidden artifical neural network) is not suitable for that\n\n##### why quantum deep learning or deep quantum learning?\n###### In the mid of Artificial Neural Network Research people realised at the maximum extreme only certain mathematical operations possible to do with ANN and the aim of this ANN is to achieve parallel execution of many mathematical operations , In artificial Intelligence ,the world intelligence stands for mathematics ,how effective if a probem can be solvable is based on the mathematics logic applying on the problem , more the logic will give more performance(more intelligent), This goal open the gate for quantum artificial neural network, On applying the ideas behind the deep learning to quantum mechanics environment, its possible to apply complex mathematical equations to n number of non relational data to find more features and can improve the performance\n \n\u003Ca name=\"qmlvsdl\">\u003C\u002Fa>\n## Quantum Machine Learning vs Deep Learning\n \n##### Its fun to discuss about this , In recent days most of the employees from Product Based Companies Like google,microsoft etc using the word deep learning ,What actually Deep Learning ? and is it a new inventions ? how to learn this ? Is it replacing machine learning ? these question come to the mind of junior research scholars and mid level employees\n \n##### The one answer to all questions is deep learning = parallel \"for\" loops ,No more than that ,Its an effective way of executing multiple tasks repeatly and to reduce the computation cost, But it introduce a big cap between mathematics and computerscience , How ? \n\n##### All classical algorithms based on serial processing ,Its depends on the feedback of the first loop ,On applying a serial classical algorithm in multiple clusters wont give a good result ,but some light weight parallel classical algorithms(Deep learning) doing the job in multiple clusters and its not suitable for complex problems, What is the solution for then? \n\n##### As in the title Quantum Machine Learning ,The advantage behind is deep learning is doing the batch processing simply on the data ,but quantum machine learning designed to do batch processing as per the algorithm\n\n##### The product companies realised this one and they started migrating to quantum machine learning and executing the classical algorithms on quantum concept gives better result than deep learning algorithms on classical computer and the target to merge both to give very wonderful result \n\n##### References\n \n* [Quora](https:\u002F\u002Fwww.quora.com\u002FHow-will-quantum-computing-revolutionize-deep-learning) - Good Discussion\n* [Quora](https:\u002F\u002Fwww.quora.com\u002FWill-quantum-computing-change-machine-learning) - The Bridge Discussion\n* [Pdf](http:\u002F\u002Fwww.scottaaronson.com\u002Fpapers\u002Fqml.pdf) - Nice Discussion\n* [Google](https:\u002F\u002Fventurebeat.com\u002F2015\u002F11\u002F11\u002Fgoogle-researcher-quantum-computers-arent-perfect-for-deep-learning\u002F) - Google Research Discussion\n* [Microsoft](http:\u002F\u002Fwww.physics.usyd.edu.au\u002Fquantum\u002FCoogee2015\u002FPresentations\u002FSvore.pdf) - Microsoft plan to merge both\n* [IBM](https:\u002F\u002Fwww.rtinsights.com\u002Fibm-quantum-computing-with-machine-learning-in-cloud\u002F) - IBM plan to merge both\n* [IBM Project](https:\u002F\u002Fwww.ibm.com\u002Fblogs\u002Fresearch\u002F2017\u002F03\u002Fquantum-algorithm-classifies-9500-handwritten-numbers\u002F) - IBM Project idea\n* [MIT and Google](https:\u002F\u002Fwww.technologyreview.com\u002Fs\u002F544421\u002Fgoogles-quantum-dream-machine\u002F) - Solutions for all questions\n \n\n\u003Ca name=\"quantummeetups\">\u003C\u002Fa>\n## QUANTUM MEETUPS\n\n* [Meetup 1](https:\u002F\u002Fwww.meetup.com\u002FQuantum-Physics-Drinks\u002F) - Quantum Physics\n* [Meetup 2](https:\u002F\u002Fwww.meetup.com\u002FLondon-Quantum-Computing-Meetup\u002F) - Quantum Computing London\n* [Meetup 3](https:\u002F\u002Fwww.meetup.com\u002FNew-York-Quantum-Computing-Meetup\u002F) - Quantum Computing New York\n* [Meetup 4](https:\u002F\u002Fwww.meetup.com\u002FQuantum-Computing-and-Big-Data\u002Fevents\u002F238749477\u002F) - Quantum Computing Canada\n* [Meetup 5](https:\u002F\u002Fwww.meetup.com\u002FAustin-Quantum-Computing-Artificial-Intelligence-Meetup\u002F) - Quantum Artificial Intelligence Texas\n* [Meetup 6](https:\u002F\u002Fwww.meetup.com\u002FThe-NY-Quantum-Theory-Group\u002F) - Genarl Quantum Mechanics , Mathematics New York\n* [Meetup 7](https:\u002F\u002Fwww.meetup.com\u002FQuantum-Computers\u002F) - Quantum Computing Mountain View California\n* [Meetup 8](https:\u002F\u002Fwww.meetup.com\u002Fnyhackr\u002F) - Statistical Analysis New York\n* [Meetup 9](https:\u002F\u002Fwww.meetup.com\u002FQuantum-Physics-Meetup-Group\u002F) - Quantum Mechanics London UK\n* [Meetup 10](https:\u002F\u002Fwww.meetup.com\u002FQuantum-Physics-Drinks\u002F) - Quantum Physics Sydney Australia\n* [Meetup 11](https:\u002F\u002Fwww.meetup.com\u002FBerkeley-Quantum-Physics-Spirituality-Meetup\u002F) - Quantum Physics Berkeley CA\n* [Meetup 12](https:\u002F\u002Fwww.meetup.com\u002FQuantumX-Quantum-Computing-Meetup\u002F) - Quantum Computing London UK\n* [Meetup 13](https:\u002F\u002Fwww.meetup.com\u002FCarmichael-Quantum-Christians\u002F) - Quantum Mechanics Carmichael CA\n* [Meetup 14](https:\u002F\u002Fwww.meetup.com\u002FRelativity-Exploration-of-Portland\u002F) - Maths and Science Group Portland\n* [Meetup 15](https:\u002F\u002Fwww.meetup.com\u002FQuantum-Physics-Discussion-Group\u002F) - Quantum Physics Santa Monica, CA\n* [Meetup 16](https:\u002F\u002Fwww.meetup.com\u002FQuantum-Vibrational-Healing\u002F) - Quantum Mechanics London\n* [Meetup 17](https:\u002F\u002Fwww.meetup.com\u002FLondon-Quantum-Computing-Meetup\u002F) - Quantum Computing London\n* [Meetup 18](https:\u002F\u002Fwww.meetup.com\u002Fquantum-metaphysics\u002F) - Quantum Meta Physics ,Kansas City , Missouri ,US\n* [Meetup 19](https:\u002F\u002Fwww.meetup.com\u002FQuantum-Content\u002F) - Quantum Mechanics and Physics ,Boston ,Massachusetts ,US\n* [Meetup 20](https:\u002F\u002Fwww.meetup.com\u002FQuantum-Organization\u002F) - Quantum Physics and Mechanics ,San Francisco ,California\n* [Meetup 21](https:\u002F\u002Fwww.meetup.com\u002FTheoretical-Quantum-Mechanics\u002F) - Quantum Mechanics ,Langhorne, Pennsylvania\n* [Meetup 22](https:\u002F\u002Fwww.meetup.com\u002FPortland-Science-Meetup\u002F) - Quantum Mechanics ,Portland\n\n \n\u003Ca name=\"quantumdegrees\">\u003C\u002Fa>\n## QUANTUM BASED DEGREES\n\n##### Plenty of courses around the world and many Universities Launching it day by day ,Instead of covering only Quantum ML , Covering all Quantum Related topics gives more idea in the order below\n \n#### Available Courses\n\n###### Quantum Mechanics for Science and Engineers\n\n* Online\n \n\t* [Standford university](http:\u002F\u002Fonline.stanford.edu\u002Fcourse\u002Fqmse01-quantum-mechanics-scientists-and-engineers) - Nice Preparatory Course\n\t* [edx](https:\u002F\u002Fcourses.edx.org\u002Fcourses\u002Fcourse-v1:GeorgetownX+PHYX-008-01x+1T2017\u002Finfo) - Quantum Mechanics for Everyone\n * [NPTEL 1](http:\u002F\u002Fnptel.ac.in\u002Fcourses\u002F115104096\u002F) - Nice Series of Courses to understand basics and backbone of quantum mechanics\n * [NPTEL 2](http:\u002F\u002Fnptel.ac.in\u002Fcourses\u002F115102023\u002F)\n * [NPTEL 3](http:\u002F\u002Fnptel.ac.in\u002Fcourses\u002F115106066\u002F)\n * [NPTEL 4](http:\u002F\u002Fnptel.ac.in\u002Fcourses\u002F115108074\u002F)\n \t* [NPTEL 5](http:\u002F\u002Fnptel.ac.in\u002Fcourses\u002F115101010\u002F)\n \n* Class Based Course\n \n\t* UK\n\n\t\t* [Bristol](http:\u002F\u002Fwww.bristol.ac.uk\u002Fmaths\u002Fstudy\u002Fundergraduate\u002Funits1617\u002Flevelh6units\u002Fquantum-mechanics-math35500\u002F)\n \n\t* Australia\n \n\t\t* [Australian National University](http:\u002F\u002Fprogramsandcourses.anu.edu.au\u002Fcourse\u002FPHYS2013)\n \n\t* Europe\n \n\t\t* [Maxs Planks University](http:\u002F\u002Fprogramsandcourses.anu.edu.au\u002Fcourse\u002FPHYS2013)\n \n###### Quantum Physics\n \n* Online\n\n\t* [MIT](https:\u002F\u002Focw.mit.edu\u002Fcourses\u002Fphysics\u002F8-04-quantum-physics-i-spring-2013\u002Flecture-videos\u002F) - Super Explanation and well basics\n * [NPTEL](http:\u002F\u002Fnptel.ac.in\u002Fcourses\u002F122106034\u002F) - Nice Series of Courses to understand basics and backbone of quantum Physics\n\n* Class Based Course\n \n\t* Europe\n\n\t\t* [University of Copenhagen](http:\u002F\u002Fwww.nbi.ku.dk\u002Fenglish\u002Fresearch\u002Fquantum-physics\u002F)\n \n###### Quantum Chemistry\n\n* Online\n\n * [NPTEL 1](http:\u002F\u002Fnptel.ac.in\u002Fcourses\u002F104108057\u002F) - Nice Series of Courses to understand basics and backbone of quantum Chemistry\n * [NPTEL 2](http:\u002F\u002Fnptel.ac.in\u002Fcourses\u002F104106083\u002F) - \n\n* Class Based Course\n \n\t* Europe\n\n\t\t* [UGent Belgium](http:\u002F\u002Fwww.quantum.ugent.be\u002F)\n \n###### Quantum Computing\n\n* Online\n\n\t* [MIT](https:\u002F\u002Focw.mit.edu\u002Fcourses\u002Fmathematics\u002F18-435j-quantum-computation-fall-2003\u002Findex.htm) - Super Explanation and well basics\n\t* [edx](https:\u002F\u002Fwww.edx.org\u002Fcourse\u002Fquantum-mechanics-quantum-computation-uc-berkeleyx-cs-191x) - Nice Explanation\n * [NPTEL](http:\u002F\u002Fnptel.ac.in\u002Fcourses\u002F104104082\u002F) - Nice Series of Courses to understand basics and backbone of quantum Computing\n\n* Class Based Course\n \n\t* Canada\n \n\t\t* [uwaterloo](https:\u002F\u002Fuwaterloo.ca\u002Finstitute-for-quantum-computing\u002F)\n\n\t* Singapore\n \n\t\t* [National University Singapore](http:\u002F\u002Fwww.quantumlah.org\u002F)\n\n\t* USA\n \n\t\t* [Berkley](http:\u002F\u002Fwww.quantumlah.org\u002F)\n \n * China\n \n * [Baidu](https:\u002F\u002Fmedium.com\u002F@Synced\u002Fbaidu-launches-institute-of-quantum-computing-899454cbe1c5)\n \n###### Quantum Technology\n\n* Class Based Course\n \n\t* Canada\n \n\t\t* [uwaterloo](https:\u002F\u002Fuwaterloo.ca\u002Finstitute-for-quantum-computing\u002F)\n\n\t* Singapore\n \n\t\t* [National University Singapore](http:\u002F\u002Fwww.quantumlah.org\u002F)\n\n\t* Europe\n \n\t\t* [Munich](http:\u002F\u002Fwww.munich-quantum-center.de\u002Findex.php?id=1)\n \n * Russia\n \n * [Skoltech](http:\u002F\u002Fcrei.skoltech.ru\u002Fcpqm)\n \n \n###### Quantum Information Science\n\n* External Links\n\n\t* [quantwiki](https:\u002F\u002Fwww.quantiki.org\u002Fwiki\u002Fcourses-quantum-information-science)\n \n* Online\n\n\t* [MIT](https:\u002F\u002Focw.mit.edu\u002Fcourses\u002Fmedia-arts-and-sciences\u002Fmas-865j-quantum-information-science-spring-2006\u002F) - Super Explanation and well basics\n\t* [edx](https:\u002F\u002Fwww.edx.org\u002Fcourse\u002Fquantum-information-science-ii-mitx-8-371x) - Nice Explanation\n * [NPTEL](http:\u002F\u002Fnptel.ac.in\u002Fcourses\u002F115101092\u002F) - Nice Series of Courses to understand basics and backbone of quantum information and computing\n\n* Class Based Course\n \n\t* USA\n \n\t\t* [MIT](http:\u002F\u002Fqis.mit.edu\u002F)\n\t\t* [Standford University](https:\u002F\u002Fweb.stanford.edu\u002Fgroup\u002Fyamamotogroup\u002F)\n \t* [Joint Center for Quantum Information and Computer Science - University of Maryland](http:\u002F\u002Fquics.umd.edu\u002F)\n \n\t* Canada\n \n\t\t* [Perimeter Institute](https:\u002F\u002Fperimeterinstitute.ca\u002Fresearch\u002Fresearch-areas\u002Fquantum-information)\n\n\t* Singapore\n \n\t\t* [National University Singapore](http:\u002F\u002Fwww.quantumlah.org\u002F)\n\n\t* Europe\n \n\t\t* [ULB Belgium](http:\u002F\u002Fquic.ulb.ac.be\u002Fteaching)\n * [IQOQI](https:\u002F\u002Fiqoqi.at\u002Fen)\n \n \n###### Quantum Electronics\n \n* Online\n\n * [MIT](https:\u002F\u002Focw.mit.edu\u002Fcourses\u002Felectrical-engineering-and-computer-science\u002F6-974-fundamentals-of-photonics-quantum-electronics-spring-2006\u002F) - Wonderful Course\n * [NPTEL](http:\u002F\u002Fnptel.ac.in\u002Fcourses\u002F115102022\u002F) - Nice Series of Courses to understand basics and backbone of quantum Electronics\n\n* Class Based Course\n \n * USA\n \n\t\t* [Texas](http:\u002F\u002Fwww.ece.utexas.edu\u002Fresearch\u002Fareas\u002Fplasma-quantum-electronics-and-optics) \n \n\t* Europe\n \n\t\t* [Zurich](http:\u002F\u002Fwww.iqe.phys.ethz.ch\u002Futils\u002Fcontact.html)\n * [ICFO](http:\u002F\u002Fquantumtech.icfo.eu\u002F) \n \n\t* Asia\n \n\t\t* [Tata Institute](http:\u002F\u002Fwww.tifr.res.in\u002F~quantro\u002Findex.html)\n \n###### Quantum Field Theory\n\n* Online\n \n\t* [Standford university](https:\u002F\u002Focw.mit.edu\u002Fcourses\u002Fphysics\u002F8-323-relativistic-quantum-field-theory-i-spring-2008\u002F) - Nice Preparatory Course\n * [edx](https:\u002F\u002Fwww.edx.org\u002Fcourse\u002Feffective-field-theory-mitx-8-eftx) - Some QFT Concepts available\n \n* Class Based Course\n \n\t* UK\n\n\t\t* [Imperial](http:\u002F\u002Fwww.imperial.ac.uk\u002Ftheoretical-physics\u002Fpostgraduate-study\u002Fmsc-in-quantum-fields-and-fundamental-forces\u002F)\n \n\t* Europe\n \n\t\t* [Vrije](http:\u002F\u002Fwww.vub.ac.be\u002Fen\u002Fstudy\u002Ffiches\u002F56659\u002Fquantum-field-theory)\n \n###### Quantum Computer Science\n \n* Class Based Course\n \n\t* USA\n\n\t\t* [Oxford](https:\u002F\u002Fwww.cs.ox.ac.uk\u002Fteaching\u002Fcourses\u002Fquantum\u002F)\n * [Joint Center for Quantum Information and Computer Science - University of Maryland](http:\u002F\u002Fquics.umd.edu\u002F)\n \n###### Quantum Artificial Intelligence and Machine Learning\n\n* External Links\n\n\t* [Quora 1](https:\u002F\u002Fwww.quora.com\u002FQuantum-Computing-vs-Artificial-Intelligence-for-a-PhD)\n * [Quora 1](https:\u002F\u002Fwww.quora.com\u002FWhere-can-you-get-a-PhD-in-quantum-machine-learning)\n * [Artificial Agents Research for Quantum Designs](https:\u002F\u002Fwww.uibk.ac.at\u002Fnewsroom\u002Fartificial-agent-designs-quantum-experiments.html.en)\n \n###### Quantum Mathematics\n\n* Class Based Course\n \n\t* USA\n\n\t\t* [University of Notre ***](http:\u002F\u002Facms.nd.edu\u002Fresearch\u002F)\n\n\n\u003Ca name=\"quantumconsolidatedresearchpapers\">\u003C\u002Fa>\n## CONSOLIDATED Quantum Research Papers\n\n* [scirate](https:\u002F\u002Fscirate.com\u002F) - Plenty of Quantum Research Papers Available\n* [Peter Wittek](http:\u002F\u002Fpeterwittek.com\u002Fbook.html) - Famous Researcher for the Quantum Machine Leanrning , Published a book in this topic\n* [Murphy Yuezhen Niu] (https:\u002F\u002Fscholar.google.com\u002Fcitations?user=0wJPxfkAAAAJ&hl=en) - A good researcher published some nice articles\n\n\u003Ca name=\"quantumconsolidatedresearchpapers\">\u003C\u002Fa>\n## Recent Quantum Updates forum ,pages and newsletter\n\n* [Quantum-Tech](https:\u002F\u002Fmedium.com\u002Fquantum-tech) - A Beautiful Newsletter Page Publishing Amazing Links \n* [facebook Quantum Machine Learning](https:\u002F\u002Fwww.facebook.com\u002Fquantummachinelearning) - Running By me . Not that much good :). You can get some ideas\n* [Linkedlin Quantum Machine Learning](https:\u002F\u002Fwww.linkedin.com\u002Fgroups\u002F8592758) - A nice page running by experts. Can get plenty of ideas\n* [FOSDEM 2019 Quantum Talks](https:\u002F\u002Ffosdem.org\u002F2019\u002Fschedule\u002Ftrack\u002Fquantum_computing\u002F) - A one day talk in fosdem 2019 with more than 10 research topics,tools and ideas\n* [FOSDEM 2020 Quantum Talks](https:\u002F\u002Ffosdem.org\u002F2020\u002Fschedule\u002Ftrack\u002Fquantum_computing\u002F) - Live talk in fosdem 2020 with plenty new research topics,tools and ideas\n\n### License\n\n[![License](http:\u002F\u002Fmirrors.creativecommons.org\u002Fpresskit\u002Fbuttons\u002F88x31\u002Fsvg\u002Fcc-zero.svg)](https:\u002F\u002Fgithub.com\u002Fkrishnakumarsekar\u002Fawesome-quantum-machine-learning\u002Fblob\u002Fmaster\u002FLICENCE)\n\n### Dedicated Opensources\n\n[![Dedicated Opensources](http:\u002F\u002Flivingintown.com\u002Fwp-content\u002Fuploads\u002Fsites\u002F1112\u002F2015\u002F03\u002Fcoming-soon-small.jpg)]()\n \n* Source code of plenty of Algortihms in Image Processing , Data Mining ,etc in Matlab, Python ,Java and VC++ Scripts\n* Good Explanations of Plenty of algorithms with flow chart etc\n* Comparison Matrix of plenty of algorithms\n* [Is Quantum Machine Learning Will Reveal the Secret Maths behind Astrology?](https:\u002F\u002Fmedium.com\u002F@krishnakumar070891\u002Fis-quantum-machine-learning-will-reveal-the-secret-maths-behind-astrology-ce69fd71a019)\n* Awesome Machine Learning and Deep Learning Mathematics is [online](https:\u002F\u002Fgithub.com\u002Fkrishnakumarsekar\u002Fawesome-machine-learning-deep-learning-mathematics)\n* Published Basic Presentation of the series Quantum Machine Learning\n[![PPT Basics](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fkrishnakumarsekar_awesome-quantum-machine-learning_readme_6ded15b48879.jpg)](https:\u002F\u002Fdocs.google.com\u002Fpresentation\u002Fd\u002F1sqQu3LhX97OIwIEEvDMpzQRh6x52C9XDs1RkbPBM9uM\u002Fpresent)\n[![PPT Basics2](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fkrishnakumarsekar_awesome-quantum-machine-learning_readme_3ac8cbac38e0.jpg)](https:\u002F\u002Fdocs.google.com\u002Fpresentation\u002Fd\u002F1TBmkOkfeIifT73p2ENtnU75JgzMXqj9sOPws378-DPc\u002Fpresent)\n\n### Contribution\n\n* If you think this page might helpful. Please help for World Education Charity or kids who wants to learn \n\u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fkrishnakumarsekar\u002Fawesome-quantum-machine-learning\u002Fblob\u002Fmaster\u002Fcontribution.md\">\u003Cimg src=\"http:\u002F\u002Fcomps.canstockphoto.com\u002Fcan-stock-photo_csp23653568.jpg\" align=\"left\" height=\"200\" width=\"200\">\u003C\u002Fa>\n","# 优秀的量子机器学习 [![Awesome](https:\u002F\u002Fcdn.rawgit.com\u002Fsindresorhus\u002Fawesome\u002Fd7305f38d29fed78fa85652e3a63e154dd8e8829\u002Fmedia\u002Fbadge.svg)](https:\u002F\u002Fgithub.com\u002Fsindresorhus\u002Fawesome)\n\n一份精心整理的优秀量子机器学习算法、学习资料、库及软件（按语言分类）列表。\n\n[![主要架构](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fkrishnakumarsekar_awesome-quantum-machine-learning_readme_eb65bb413326.png)](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1611.09347.pdf)\n\n[![量子核](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fkrishnakumarsekar_awesome-quantum-machine-learning_readme_6bed26ea92c2.jpg)](https:\u002F\u002Fwww.dwavesys.com\u002Ftutorials\u002Fbackground-reading-series\u002Fquantum-computing-primer)\n\n[![深入物理对比](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fkrishnakumarsekar_awesome-quantum-machine-learning_readme_4b920643d9b1.jpg)](https:\u002F\u002Fpatents.google.com\u002Fpatent\u002FUS20060091375)\n\n\n## 目录\n\n\u003C!-- MarkdownTOC depth=4 -->\n\n- [导言](#introduction)\n - [为什么是量子机器学习？](#introduction-why-quantum-machine-learning)\n- [基础](#basics)\n - [什么是量子力学？](#basics-what-quantum-mechanics)\n - [什么是量子计算？](#basics-what-quantum-computing)\n - [什么是拓扑量子计算？](#basics-what-topological-quantum-computing)\n - [量子计算与经典计算的对比](#basics-quantum-classical-vs) \n- [量子计算](#quantumcomputing)\n - [原子结构](#quantumcomputing-atom-structure)\n - [光子波](#quantumcomputing-photon-wave)\n - [电子涨落或自旋](#quantumcomputing-elecfluctuation-spin)\n - [量子态](#quantumcomputing-states)\n - [叠加态](#quantumcomputing-superposition)\n - [专用于机器学习的叠加态（量子行走）](#quantumcomputing-superpostion-machinelearning)\n - [经典比特](#quantumcomputing-classicalbit)\n - [量子比特或Qubit](#quantumcomputing-qubit)\n - [量子计算中的基本门](#quantumcomputing-basicgates)\n - [量子二极管](#quantumcomputing-diode)\n - [量子晶体管](#quantumcomputing-transistor)\n - [量子处理器](#quantumcomputing-processor)\n - [量子寄存器QRAM](#quantumcomputing-qram) \n - [量子纠缠](#quantumcomputing-entanglement)\n- [量子计算与机器学习的桥梁](#qcmlbridge)\n - [复数](#qcmlbridge-complexNumbers)\n - [张量](#qcmlbridge-tensors)\n - [张量网络](#qcmlbridge-tensors-network) \n - [预言机](#qcmlbridge-oracle)\n - [哈达玛变换](#qcmlbridge-hadamard)\n - [希尔伯特空间](#qcmlbridge-hilbert)\n - [特征值与特征向量](#qcmlbridge-eigen)\n - [薛定谔算符](#qcmlbridge-schrodinger)\n - [量子λ演算](#qcmlbridge-lamda)\n - [量子振幅与相位](#qcmlbridge-amp-phase)\n - [量子比特的编码与解码](#qcmlbridge-encode-decode)\n - [将经典比特转换为量子比特](#qcmlbridge-classical-qubit)\n - [量子狄拉克符号与基矢](#qcmlbridge-dirac-ket)\n - [量子复杂性](#qcmlbridge-complexity)\n - [任意量子态的生成](#qcmlbridge-arbitarystategeneration)\n- [量子算法](#quantumalgorithms)\n - [量子傅里叶变换](#quantumalgorithms-fourier)\n - [变分量子本征求解器](#quantumalgorithms-quantumeigensolver)\n - [格罗弗算法](#quantumalgorithms-grover)\n - [肖尔算法](#quantumalgorithms-shors)\n - [哈密顿量预言机模型](#quantumalgorithms-hamiltonian)\n - [伯恩斯坦-瓦齐拉尼算法](#quantumalgorithms-Bernsteinvazirani)\n - [西蒙算法](#quantumalgorithms-simons)\n - [多伊奇-乔萨算法](#quantumalgorithms-deutschjozsa)\n - [梯度下降法](#quantumalgorithms-gradient-descent) \n - [相位估计算法](#quantumalgorithms-phase-estimation)\n - [哈尔变换](#quantumalgorithms-haar)\n - [量子脊波变换](#quantumalgorithms-ridgelet)\n - [量子NP问题](#quantumalgorithms-npproblem)\n- [量子机器学习算法](#quantumalgorithmsml)\n - [量子K近邻](#quantumalgorithmsml-qknn)\n - [量子K均值](#quantumalgorithmsml-kmeans)\n - [量子模糊C均值](#quantumalgorithmsml-qfcm)\n - [量子支持向量机](#quantumalgorithmsml-svm)\n - [量子遗传算法](#quantumalgorithmsml-genetic)\n - [量子隐马尔可夫模型](#quantumalgorithmsml-hmm)\n - [基于贝叶斯方法的量子态分类](#quantumalgorithmsml-bayesian)\n - [量子蚁群优化](#quantumalgorithmsml-antcolony)\n - [量子细胞自动机](#quantumalgorithmsml-caautomata)\n - [利用主成分分析进行量子分类](#quantumalgorithmsml-pca)\n - [受量子启发的进化算法](#quantumalgorithmsml-evolutionary)\n - [量子近似优化算法](#quantumalgorithmsml-qaoa)\n - [量子大象放牧优化](#quantumalgorithmsml-qeho)\n - [量子行为粒子群优化](#quantumalgorithmsml-qpso)\n - [量子退火期望最大化](#quantumalgorithmsml-qaem)\n- [量子神经网络](#qnn)\n - [量子感知器](#qnn-perceptron)\n - [Qurons](#qnn-qurons)\n - [量子自编码器](#qnn-autoencoder)\n - [量子退火](#qnn-annealing)\n - [光量子神经网络的实现](#qnn-photonicqnn)\n - [量子前馈神经网络](#qnn-feedforward)\n - [量子玻尔兹曼神经网络](#qnn-boltzman)\n - [量子神经网络的权重存储](#qnn-weightstorage)\n - [量子倒置神经网络](#qnn-upsidedown)\n - [量子哈密顿神经网络](#qnn-hamiltoniannet)\n - [QANN](#qnn-qann)\n - [QPN](#qnn-qpn)\n - [SAL](#qnn-sal)\n - [量子哈密顿学习](#qnn-hamiltonianlearning)\n - [压缩量子哈密顿学习](#qnn-compressedhamiltonianlearning)\n- [量子统计数据分析](#quantumstatistics)\n\t- [量子概率论](#quantumstatistics-probabilitytheory)\n - [科尔莫戈洛夫理论](#quantumstatistics-kolmogorovian)\n - [量子测量问题](#quantumstatistics-measurementproblem)\n - [直觉逻辑](#quantumstatistics-intuitionistic)\n - [海廷代数](#quantumstatistics-heytingalgebra)\n - [量子滤波](#quantumstatistics-quantumfiltering)\n - [悖论](#quantumstatistics-paradoxes) \n - [量子随机过程](#quantumstatistics-stochasticprocess)\n - [双重否定](#quantumstatistics-doublenegation)\n - [量子随机微积分](#quantumstatistics-stochasticcalculus)\n - [哈密顿微积分](#quantumstatistics-hamiltoniancalculus)\n - [量子伊藤公式](#quantumstatistics-itosformula)\n - [量子随机微分方程(QSDE)](#quantumstatistics-qsde)\n - [量子随机积分](#quantumstatistics-stochasticintegration)\n - [伊藤积分](#quantumstatistics-itōintegral)\n - [拟概率分布](#quantumstatistics-quasiprobabilitydistributions)\n - [量子维纳过程](#quantumstatistics-quantumwienerprocesses)\n - [量子统计系综](#quantumstatistics-statisticalensemble)\n \t- [量子密度算符或密度矩阵](#quantumstatistics-densityoperator)\n - [吉布斯正则系综](#quantumstatistics-gibbscanonicalensemble)\n - [量子均值](#quantumstatistics-mean)\n - [量子方差](#quantumstatistics-variance)\n - [不变性](#quantumstatistics-envariance)\n - [多项式优化](#quantumstatistics-polynomialoptimization)\n - [二次无约束二进制优化](#quantumstatistics-qubo)\n - [量子梯度下降](#quantumstatistics-quantumgradientdescent)\n - [基于量子的牛顿法用于约束优化](#quantumstatistics-newtonmethodconstrainedoptimization)\n - [基于量子的牛顿法用于无约束优化](#quantumstatistics-newtonmethodunconstrainedoptimization)\n - [量子系综](#quantumstatistics-quantumensemble)\n - [量子拓扑](#quantumstatistics-quantumtopology)\n - [量子拓扑数据分析](#quantumstatistics-quantumtopologicaldataanalysis)\n - [量子贝叶斯假设](#quantumstatistics-quantumbayesianhypothesis)\n - [量子统计决策理论](#quantumstatistics-quantumstatisticaldecisiontheory)\n - [量子极小极大定理](#quantumstatistics-quantumminimaxtheorem)\n - [量子亨特-斯坦定理](#quantumstatistics-quantumhuntsteintheorem)\n - [量子局部渐近正态性](#quantumstatistics-quantumlocalasymptoticnormality)\n - [量子伊辛模型](#quantumstatistics-isingmodel)\n - [量子梅特罗波利斯采样](#quantumstatistics-metropolissampling)\n - [量子蒙特卡洛近似](#quantumstatistics-montecarloapproximation)\n - [量子自助法](#quantumstatistics-bootstrapping)\n - [量子自助聚合](#quantumstatistics-bootstrapaggregation)\n - [量子决策树分类器](#quantumstatistics-decisiontreeclassifier)\n - [量子异常检测](#quantumstatistics-outlierdetection)\n - [量子化学中的Cholesky分解](#quantumstatistics-choleskydecomposition)\n - [量子统计推断](#quantumstatistics-quantumstatisticalinference)\n - [渐近量子统计推断](#quantumstatistics-quantumstatisticalinferenceasymptotic)\n - [量子高斯混合模型](#quantumstatistics-qgmm)\n - [量子t-design](#quantumstatistics-quantumtdesign)\n - [量子中心极限定理](#quantumstatistics-quantumcentrallimittheorem)\n - [量子假设检验](#quantumstatistics-quantumhypothesistesting)\n - [量子卡方检验与拟合优度检验](#quantumstatistics-quantumchisquared)\n \t- [量子估计理论](#quantumstatistics-quantumestimationtheory)\n - [量子线性回归](#quantumstatistics-quantumlinearregression)\n - [量子的渐近性质](#quantumstatistics-quantumasymptoticproperties)\n - [量子概念中的异常检测](#quantumstatistics-quantumoutlier)\n- [量子人工智能](#quantumai)\n\t- [启发式量子力学](#quantumai-heuristicmechanics)\n - [一致的量子推理](#quantumai-quantumreasoning)\n - [量子强化学习](#quantumai-reinforcementlearning)\n- [量子计算机视觉](#quantumcv)\n- [量子编程语言、工具和软件](#qpl)\n - [全部](#qpl-all)\n- [量子算法源代码、GitHub](#quantumsourcecode)\n- [量子热点话题](#quantumhottopics)\n - [量子认知](#quantumhottopics-cognition)\n - [量子相机](#quantumhottopics-camera)\n - [量子数学](#quantumhottopics-mathematics)\n - [量子信息处理](#quantumhottopics-informationprocessing)\n - [量子图像处理](#quantumhottopics-imageprocessing)\n - [量子密码学](#quantumhottopics-cryptography)\n - [量子弹性搜索](#quantumhottopics-elasticsearch)\n - [量子DNA计算](#quantumhottopics-dna)\n - [绝热量子计算](#quantumhottopics-adiabetic)\n - [利用量子进行拓扑大数据分析](#quantumhottopics-topologicalbigdata)\n - [基于哈密顿量的时间量子计算](#quantumhottopics-hamiltoniancomputing)\n - [深度量子学习](#quantumhottopics-deepquantumlearning)\n - [量子隧穿效应](#quantumhottopics-tunneling)\n - [量子纠缠](#quantumhottopics-entanglment)\n \t- [量子本征谱](#quantumhottopics-eigenspectrum)\n - [量子点](#quantumhottopics-dots)\n - [量子电动力学](#quantumhottopics-electrodynamics)\n - [量子 teleportation](#quantumhottopics-teleportation)\n - [量子霸权](#quantumhottopics-supremacy)\n - [量子芝诺效应](#quantumhottopics-zenoeffect)\n - [量子上同调](#quantumhottopics-cohomology)\n - [量子色动力学](#quantumhottopics-chromodynamics)\n - [量子达尔文主义](#quantumhottopics-darwinism)\n - [量子相干性](#quantumhottopics-coherence)\n - [量子退相干](#quantumhottopics-decoherence) \n - [拓扑量子计算](#quantumhottopics-topologicalcomputing)\n - [拓扑量子场论](#quantumhottopics-topologicalfieldtheory)\n - [量子纽结](#quantumhottopics-knots)\n - [拓扑量子纠缠](#quantumhottopics-topologicalentanglment)\n - [玻色采样](#quantumhottopics-bosonsampling)\n - [量子卷积码](#quantumhottopics-convolutionalcode)\n - [稳定子码](#quantumhottopics-stabilizercode)\n - [量子混沌](#quantumhottopics-chaos)\n - [量子博弈论](#quantumhottopics-quantumgametheory)\n - [量子信道](#quantumhottopics-quantumchannel)\n - [张量空间理论](#quantumhottopics-tensorspacetheory)\n - [量子跃迁](#quantumhottopics-quantumleap)\n - [用于时间旅行的量子力学](#quantumhottopics-quantumtimetravel)\n - [量子安全区块链](#quantumhottopics-quantumblockchain)\n - [量子互联网](#quantumhottopics-quantuminternet)\n - [量子光网络](#quantumhottopics-quantumopticalnetwork)\n - [量子干涉](#quantumhottopics-quantuminterference)\n - [量子光网络](#quantumhottopics-quantumopticalnetwork)\n - [量子操作系统](#quantumhottopics-quantumoperatingsystem)\n - [电子分数化](#quantumhottopics-electronfractionalization)\n \t- [翻转量子计算机](#quantumhottopics-flipflopquantumcomputer)\n - [基于高斯态的量子信息](#quantumhottopics-quantuminformationgaussianstates)\n - [量子异常检测](#quantumhottopics-quantumanomalydetection)\n \t- [分布式安全量子机器学习](#quantumhottopics-distributedsecureqml)\n - [去中心化量子机器学习](#quantumhottopics-decentralizedqml)\n - [用于量子设计的人工智能代理](#quantumhottopics-artificialagents)\n - [基于光的量子芯片用于AI训练](#quantumhottopics-quantumlightchipsai)\n- [面向机器学习的量子态制备算法](#quantumstatepreparationalgorithm)\n - [纯量子态](#quantumstatepreparationalgorithm-purequantumstate)\n - [可乘态](#quantumstatepreparationalgorithm-productstate)\n - [矩阵乘积态](#quantumstatepreparationalgorithm-matrixproductstate)\n - [格林伯格-霍恩-蔡林格态](#quantumstatepreparationalgorithm-Greenberger)\n - [W态](#quantumstatepreparationalgorithm-wstate)\n - [AKLT模型](#quantumstatepreparationalgorithm-akltmodel)\n - [马朱姆达尔-戈什模型](#quantumstatepreparationalgorithm-majumdarmodel)\n - [多态兰道-泽纳模型](#quantumstatepreparationalgorithm-Landauzenermodels)\n - [投影纠缠对态](#quantumstatepreparationalgorithm-peps)\n - [无限投影纠缠对态](#quantumstatepreparationalgorithm-ipeps)\n - [角转移矩阵方法](#quantumstatepreparationalgorithm-cornertransfermatrix)\n - [张量纠缠重整化](#quantumstatepreparationalgorithm-tensorentanglerenormaization)\n - [用于监督学习的树状张量网络](#quantumstatepreparationalgorithm-treetensornetwork)\n- [量子机器学习与深度学习的对比](#qmlvsdl)\n- [量子聚会](#quantummeetups)\n- [量子Google小组](#quantumgroups)\n- [量子相关公司](#quantumcompanies)\n- [量子LinkedIn](#quantumlinkedlin)\n- [量子相关学位](#quantumdegrees)\n- [量子ML书籍汇总](#quantumconsolidatedbooks)\n- [量子ML视频汇总](#quantumconsolidatedvideos)\n- [量子ML研究论文汇总](#quantumconsolidatedresearchpapers)\n- [量子ML研究科学家汇总](#quantumconsolidatedresearchscientist)\n- [近期量子更新论坛、页面和通讯](#quantumforumsnewsletter)\n\n\u003C!-- \u002FMarkdownTOC -->\n\n\u003Ca name=\"introduction\">\u003C\u002Fa>\n\n\n## 引言\n\n\u003Ca name=\"introduction-why-quantum-machine-learning\">\u003C\u002Fa>\n#### 为什么是量子机器学习？\n\n##### 机器学习（ML）虽然是近年来才广为使用的术语，但其研究工作实际上可以追溯到18世纪。\n\n##### 那么，什么是机器学习呢？简单来说，就是让计算机或应用程序能够自我学习。那么，它是否完全属于计算机科学和信息技术等计算领域呢？答案是否定的。机器学习是一个跨领域的平台，渗透到生活的方方面面，从农业到机械工程等各个行业。虽然计算技术是高效、便捷地应用机器学习的关键组成部分，但更准确地说，机器学习的“母亲”其实是数学。正是数学中那些伟大的发明，比如复数理论，催生了这一领域。将数学应用于实际问题，往往能够找到解决方案。从神经网络到复杂的DNA结构，都遵循着特定的数学公式和定理。\n\n##### 随着计算技术的飞速发展，数学逐渐融入其中，并通过计算手段为现实世界的问题提供解决方案。在计算技术的发展历程中，每当达到一定技术突破时，人们便会尝试引入更为先进的数学概念，如复数、特征值等，这便成为机器学习领域发展的起点，例如人工神经网络、DNA计算等。\n\n##### 那么，如今为何这个领域会如此迅速地蓬勃发展呢？从商业角度来看，大约8到10年前，机器学习刚刚起步时，最大的障碍在于如何将数学与计算机技术相结合。当时的情况是：计算机领域的从业者对数学知之甚少，而数学研究人员又不了解计算机技术的实际应用。这种状况也反映在当时的教育体系和就业机会上。即便有人同时学习这两门学科，最终开发出的产品也难以具备良好的商业价值。\n\n##### 于是，谷歌、IBM、微软等顶尖科技公司决定组建跨学科团队，由数学家、物理学家和计算机科学家共同合作，探索机器学习领域的各种可能性。这些团队的成功催生了一系列优秀产品，并开始通过云服务的形式向用户提供相关解决方案。如今，我们正处于这样的阶段（参见[Google Cloud Vision](https:\u002F\u002Fcloud.google.com\u002Fvision\u002F)）。\n\n##### 那么，接下来会怎样呢？尽管数学已经发展到了涉及时间旅行等前沿概念的水平，但目前的计算技术仍然基于经典力学原理。各大公司意识到，计算领域必须从经典计算迈向量子计算，因此纷纷投入量子计算的研究与开发。这一新兴领域被市场称为“量子信息科学”。谷歌和IBM率先推出了用于构建量子神经网络的量子计算处理器（D-Wave）。可以预见，在未来的十年里，量子计算机科学和量子信息科学将对人工智能领域产生深远的影响。让我们拭目以待……（参考：[谷歌](https:\u002F\u002Fresearch.google.com\u002Fpubs\u002FQuantumAI.html)、[IBM](http:\u002F\u002Fresearch.ibm.com\u002Fibm-q\u002F)）。\n\n##### 参考资料\n\n* [D-Wave](https:\u002F\u002Fwww.dwavesys.com\u002Fquantum-computing) - 量子处理器制造商\n* [谷歌](https:\u002F\u002Fresearch.google.com\u002Fpubs\u002FQuantumAI.html) - 量子人工智能实验室\n* [IBM](http:\u002F\u002Fresearch.ibm.com\u002Fibm-q\u002F) - 量子计算机实验室\n* [Quora](https:\u002F\u002Fwww.quora.com\u002FIs-quantum-computing-the-future-of-AI) - 关于量子人工智能未来发展的讨论\n* [NASA](https:\u002F\u002Fti.arc.nasa.gov\u002Ftech\u002Fdash\u002Fphysics\u002Fquail\u002F) - NASA量子计算项目\n* [YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=CMdHDHEuOUE) - 谷歌发布的量子处理器视频\n* [外部链接](http:\u002F\u002Fwww.huffingtonpost.com\u002F2013\u002F07\u002F29\u002Fquantum-computers-ai-artificial-intelligence-studies_n_3664011.html) - MIT的相关评论\n* [微软新产品](https:\u002F\u002Fwww.microsoft.com\u002Fen-us\u002Fquantum) - 微软最新推出的量子语言及开发工具包\n* [微软](https:\u002F\u002Fwww.microsoft.com\u002Fen-us\u002Fresearch\u002Fproject\u002Flanguage-integrated-quantum-operations-liqui\u002F) - 微软在量子计算领域的相关研究\n* [谷歌2](https:\u002F\u002Fresearch.googleblog.com\u002F2009\u002F12\u002Fmachine-learning-with-quantum.html) - 谷歌关于量子机器学习的博客文章\n* [BBC](http:\u002F\u002Fwww.bbc.co.uk\u002Fprogrammes\u002Fp052800h) - 关于谷歌量子霸权、IBM量子计算机和微软Q的报道\n* [谷歌量子霸权](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=-ZNEzzDcllU) - 谷歌2019年实现量子霸权的最新成果\n* [IBM量子霸权](https:\u002F\u002Fwww.ibm.com\u002Fblogs\u002Fresearch\u002F2019\u002F10\u002Fon-quantum-supremacy\u002F) - IBM关于量子霸权的介绍性文章\n* [VICE关于争论的文章](https:\u002F\u002Fwww.vice.com\u002Fen_in\u002Farticle\u002Fvb5jxd\u002Fwhy-ibm-thinks-google-hasnt-achieved-quantum-supremacy) - IBM对谷歌量子霸权声明的回应\n* [IBM苏黎世量子安全加密](https:\u002F\u002Fwww.zurich.ibm.com\u002Fsecurityprivacy\u002Fquantumsafecryptography.html) - 一项有趣的初创项目，旨在通过云端和IBM Q技术取代现有的证书颁发机构系统\n\n\u003Ca name=\"basics\">\u003C\u002Fa>\n## 基础知识\n\n\u003Ca name=\"basics-what-quantum-mechanics\">\u003C\u002Fa>\n#### 什么是量子力学？\n \n##### 简单来说：当电子脱离原子运动时，遵循的是经典力学；而当电子在原子内部振动时，则属于量子力学范畴。\n\n* [维基百科](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FQuantum_mechanics) - 基本历史与概述\n* [LiveScience](http:\u002F\u002Fwww.livescience.com\u002F33816-quantum-mechanics-explanation.html) - 相关综述\n* [YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=7u_UQG1La1o) - 一段解释量子力学的精彩动画视频\n\n\u003Ca name=\"basics-what-quantum-computing\">\u003C\u002Fa>\n#### 什么是量子计算？\n \n##### 量子计算是一种利用量子比特在同一时间内并行执行多个任务的方式，它可以显著缩短计算时间，并可能使处理器的尺寸缩小到神经元级别。\n\n* [维基百科](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FQuantum_computing) - 基本历史与概述\n* [Webopedia](http:\u002F\u002Fwww.webopedia.com\u002FTERM\u002FQ\u002Fquantum_computing.html) - 相关综述\n* [YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=g_IaVepNDT4) - 一段解释量子计算的精彩动画视频\n\n\u003Ca name=\"basics-quantum-classical-vs\">\u003C\u002Fa>\n#### 量子计算与经典计算的对比\n \n* [链接](http:\u002F\u002Fwww.thphys.nuim.ie\u002Fstaff\u002Fjoost\u002FTQM\u002FQvC.html) - 基本概述\n\n \n\u003Ca name=\"quantumcomputing\">\u003C\u002Fa>\n## 量子计算\n\n\u003Ca name=\"quantumcomputing-atom-structure\">\u003C\u002Fa>\n#### 原子结构\n \n##### 简而言之：电子以椭圆形轨道绕原子核运行。\n\n* [YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=g_IaVepNDT4) - 一段关于原子基本结构的精美动画视频\n\n[![原子](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fkrishnakumarsekar_awesome-quantum-machine-learning_readme_db1bb139523e.gif)](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FAtom)\n \n\u003Ca name=\"quantumcomputing-photon-wave\">\u003C\u002Fa>\n#### 光子波\n \n##### 一句话：光通常被称为波，以光子的形式传播，类似于固体粒子中的原子。\n \n* [YOUTUBE](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=fwXQjRBLwsQ) - 一段关于基本光子的精彩动画视频1 \n* [YOUTUBE](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=KKr91v7yLcM) - 一段关于基本光子的精彩动画视频2\n \n[![光子波](https:\u002F\u002Fwww.wired.com\u002Fimages_blogs\u002Fwiredscience\u002F2013\u002F07\u002Fphoton1.jpg)](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FPhoton)\n \n\u003Ca name=\"quantumcomputing-elecfluctuation-spin\">\u003C\u002Fa>\n#### 电子波动或自旋\n \n##### 一句话：当激光照射到固体颗粒上时，原子中的电子会在原子的不同轨道层之间发生自旋。\n\n[![自旋](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fkrishnakumarsekar_awesome-quantum-machine-learning_readme_cccb4a13ac40.gif)](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FSpin_(physics))\n \n* [YOUTUBE](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=J3xLuZNKhlY) - 一段关于基本电子自旋的精彩动画视频1 \n* [YOUTUBE](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=3k5IWlVdMbo) - 一段关于基本电子自旋的精彩动画视频2\n* [YOUTUBE](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=jvvkomcmyuo) - 一段关于基本电子自旋的精彩动画视频3\n \n\u003Ca name=\"quantumcomputing-states\">\u003C\u002Fa>\n#### 状态\n \n##### 一句话：在旋转的电子上标记一个点，如果该点位于顶部，则为状态1；位于底部则为状态0。\n\n[![状态](https:\u002F\u002F3c1703fe8d.site.internapcdn.net\u002Fnewman\u002Fgfx\u002Fnews\u002Fhires\u002F2016\u002Fadeeplookint.jpg)](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FQuantum_state)\n \n* [YOUTUBE](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=sICXOwOwS4E) - 一段关于量子状态的精彩动画视频\n \n\u003Ca name=\"quantumcomputing-superposition\">\u003C\u002Fa>\n#### 超叠加\n \n##### 两句话：在电子自旋过程中，该点可能处于上下位置的中间。因此，需要根据该点的位置来决定其值是0还是1。更好的方法是结合其他电子，利用概率进行分析，这被称为超叠加。\n\n[![超叠加](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fkrishnakumarsekar_awesome-quantum-machine-learning_readme_cb4f7ca4c3aa.gif)](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FQuantum_superposition)\n \n* [YOUTUBE](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=hkmoZ8e5Qn0) - 一段关于量子超叠加的精彩动画视频\n \n\u003Ca name=\"quantumcomputing-superpostion-machinelearning\">\u003C\u002Fa>\n#### 专用于机器学习的超叠加（量子行走）\n \n##### 一句话：由于计算复杂性，量子计算通常只考虑有限数量电子之间的超叠加。若需融合多组数据，量子行走便成为一种可行的思路。\n\n[![专用于机器学习的超叠加](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fkrishnakumarsekar_awesome-quantum-machine-learning_readme_a53661c1d524.gif)](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FQuantum_walk)\n \n* [YOUTUBE](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=86QsYPxoBow) - 一段关于量子行走的精彩视频\n \n\u003Ca name=\"quantumcomputing-classicalbit\">\u003C\u002Fa>\n#### 经典比特\n \n##### 一句话：如果电子从一个原子移动到另一个原子，从基态跃迁到激发态，则使用比特值1；否则使用比特值0。\n\n[![经典比特](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fkrishnakumarsekar_awesome-quantum-machine-learning_readme_d5ea90517510.gif)](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FBit)\n \n\u003Ca name=\"quantumcomputing-qubit\">\u003C\u002Fa>\n#### 量子比特\n \n##### 一句话：一组电子状态的超叠加值即为量子比特。\n\n[![量子比特](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fkrishnakumarsekar_awesome-quantum-machine-learning_readme_5b774b87db5b.png)](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FQubit)\n \n* [YOUTUBE](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=zNzzGgr2mhk) - 一段关于量子比特的精彩视频1\n* [YOUTUBE](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=F8U1d2Hqark&t=179s) - 一段关于比特与量子比特的精彩视频2\n \n\u003Ca name=\"quantumcomputing-basicgates\">\u003C\u002Fa>\n#### 量子计算中的基本门\n \n##### 一句话：类似于经典的非门、或门和与门，量子门也可以实现类似的逻辑功能，如非门、哈达马门、交换门、相位门等。\n\n[![量子计算中的基本门](http:\u002F\u002Fwww.mdpi.com\u002Fentropy\u002Fentropy-16-05290\u002Farticle_deploy\u002Fhtml\u002Fimages\u002Fentropy-16-05290f1-1024.png)](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FQuantum_gate)\n \n* [YOUTUBE](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=2Qsh_w2kq9Y) - 一段关于量子门的精彩视频\n \n\u003Ca name=\"quantumcomputing-diode\">\u003C\u002Fa>\n#### 量子二极管\n \n##### 一句话：量子二极管采用与普通二极管不同的原理，一束激光光子会触发电子自旋，而量子磁通量则会捕获相关信息。\n\n[![量子计算中的二极管1](https:\u002F\u002Fwww.ifw-dresden.de\u002Fuserfiles\u002Fgroups\u002Fiin_folder\u002Fresearch\u002Fphotonics\u002Fpicture_laser_iin.jpg)](http:\u002F\u002Fonlinelibrary.wiley.com\u002Fdoi\u002F10.1002\u002Fadma.201200537\u002Fabstract)\n[![量子计算中的二极管2](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fkrishnakumarsekar_awesome-quantum-machine-learning_readme_ae452ae2e4ad.jpg)](https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fncomms12978)\n[![量子计算中的二极管3](https:\u002F\u002F3c1703fe8d.site.internapcdn.net\u002Fnewman\u002Fgfx\u002Fnews\u002Fhires\u002F2013\u002Fnanoscaleeng.jpg)](https:\u002F\u002Fphys.org\u002Fnews\u002F2013-10-nanoscale-boosts-quantum-dot-emitting.html)\n\n \n* [YOUTUBE](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=doyK1olswX4) - 一段关于量子二极管的精彩视频\n \n\u003Ca name=\"quantumcomputing-transistor\">\u003C\u002Fa>\n#### 量子晶体管\n \n##### 一句话：传统的晶体管通常有源极、漏极和栅极，而在量子晶体管中，源极是光子波，漏极是磁通量，而栅极则是连接经典比特与量子比特的部分。\n\n[![量子晶体管1](https:\u002F\u002Fimages.sciencedaily.com\u002F2010\u002F05\u002F100514075106_1_900x600.jpg)](http:\u002F\u002Fwww.mpq.mpg.de\u002F4987844\u002Fqip)\n[![量子晶体管2](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fkrishnakumarsekar_awesome-quantum-machine-learning_readme_2eb4740f91c9.png)](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FMagnetic_flux_quantum)\n\n* [QUORA](https:\u002F\u002Fwww.quora.com\u002FWhat-is-the-equivalent-of-the-transistor-in-a-quantum-computer) - 关于量子晶体管的讨论\n* [YOUTUBE](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=ZTxR2n2mvjc) - 讲解清晰\n\n\u003Ca name=\"quantumcomputing-processor\">\u003C\u002Fa>\n#### 量子处理器\n\n##### 一句话：由冷却装置环绕以降低巨大热量的纳米级集成电路，用于执行量子门操作。\n\n[![量子处理器1](https:\u002F\u002Fwww.dwavesys.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fcooling-082015%20copy.png)](https:\u002F\u002Fwww.dwavesys.com\u002Ftutorials\u002Fbackground-reading-series\u002Fintroduction-d-wave-quantum-hardware#h2-0)\n[![量子处理器2](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fkrishnakumarsekar_awesome-quantum-machine-learning_readme_7855ddc7a47c.png)](https:\u002F\u002Fquantumexperience.ng.bluemix.net\u002Fqstage\u002F?cm_mc_uid=36641337812614766932472&cm_mc_sid_50200000=1493295650#\u002Fuser-guide)\n[![量子处理器3](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fkrishnakumarsekar_awesome-quantum-machine-learning_readme_bb40f49a6b99.jpg)](https:\u002F\u002Fwww.cbinsights.com\u002Fblog\u002Fquantum-computing-corporations-list\u002F)\n\n* [YOUTUBE](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=CMdHDHEuOUE) - 讲解清晰\n\n\u003Ca name=\"quantumcomputing-qram\">\u003C\u002Fa>\n#### 量子随机存取存储器（QRAM）\n\n##### 一句话：与普通RAM相比，它速度极快且体积非常小。其地址可以通过量子比特的叠加态来访问；对于超大规模内存，可以使用相干叠加（地址的地址）。\n\n[![QRAM1](https:\u002F\u002Fai2-s2-public.s3.amazonaws.com\u002Ffigures\u002F2016-11-08\u002F94d487c6ef4d0fa5594eff352aac19e2bfd47ffa\u002F2-Figure1-1.png)](https:\u002F\u002Farxiv.org\u002Fpdf\u002F0708.1879.pdf)\n[![QRAM2](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fkrishnakumarsekar_awesome-quantum-machine-learning_readme_f704793f04e3.png)](https:\u002F\u002Fwww.researchgate.net\u002Fpublication\u002F51394884_Quantum_Random_Access_Memory)\n\n* [PDF](https:\u002F\u002Farxiv.org\u002Fpdf\u002F0807.4994.pdf) - 解释得非常透彻\n\n\n\u003Ca name=\"qcmlbridge\">\u003C\u002Fa>\n\n\n## 量子计算与机器学习的桥梁\n\n\n\u003Ca name=\"qcmlbridge-complexNumbers\">\u003C\u002Fa>\n#### 复数\n\n##### 一句话：通常波的干涉发生在n维结构中。为了找到多项式方程的n阶曲线，复数是一个更好的选择。\n\n[![复数1](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fkrishnakumarsekar_awesome-quantum-machine-learning_readme_2d232779e745.gif)](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FComplex_number)\n[![复数2](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fkrishnakumarsekar_awesome-quantum-machine-learning_readme_c1057a795508.png)](https:\u002F\u002Fwww.mathsisfun.com\u002Fnumbers\u002Fcomplex-numbers.html)\n[![复数3](http:\u002F\u002Fwww.mathwarehouse.com\u002Falgebra\u002Fcomplex-number\u002Fimages\u002Fgraphs\u002Fdiagram-complex-plane.png)](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FComplex_number)\n\n* [YOUTUBE](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=T647CGsuOVU) - 系列视频讲解精彩，深入浅出\n\n\u003Ca name=\"qcmlbridge-tensors\">\u003C\u002Fa>\n#### 张量\n\n##### 一句话：向量在二维空间中有方向性。而在n维空间中，张量可以用来描述向量的方向。要解决n个电子自旋空间的叠加问题，最好的方法是将向量表示为张量，并进行张量微积分运算。\n\n[![张量1](https:\u002F\u002Fi.stack.imgur.com\u002F5QsMD.png)](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FTensor)\n[![张量2](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fkrishnakumarsekar_awesome-quantum-machine-learning_readme_9e764d96aa6f.jpg)](https:\u002F\u002Fwww.quantiki.org\u002Fwiki\u002Ftensor-product)\n[![张量3](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fkrishnakumarsekar_awesome-quantum-machine-learning_readme_2d4eb2aca44e.png)](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FTensor_product)\n[![张量4](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fkrishnakumarsekar_awesome-quantum-machine-learning_readme_2d4eb2aca44e.png)](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FTensor_product_of_Hilbert_spaces)\n\n* [YOUTUBE](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=f5liqUk0ZTw) - 讲解精彩的张量基础\n* [YOUTUBE](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=xzG6c96PsLs) - 量子张量基础\n\n\u003Ca name=\"qcmlbridge-tensors-network\">\u003C\u002Fa>\n#### 张量网络\n\n##### 一句话：就像连接多个向量一样，多个张量也可以形成网络。通过求解这样的网络，可以降低处理量子比特的复杂度。\n\n[![张量网络1](http:\u002F\u002Fwww.cse.unsw.edu.au\u002F~billw\u002Fcs9444\u002Ftensor-stuff\u002Ftensor-intro-0418.gif)](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1306.2164.pdf)\n[![张量网络2](http:\u002F\u002Fwww.quantuminfo.physik.rwth-aachen.de\u002Fglobal\u002Fshow_picture.asp?id=aaaaaaaaaaiejix)](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FTensor_network_theory)\n[![张量网络3](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fkrishnakumarsekar_awesome-quantum-machine-learning_readme_2d4eb2aca44e.png)](https:\u002F\u002Fwww2.warwick.ac.uk\u002Ffac\u002Fsci\u002Fphysics\u002Fcurrent\u002Fpostgraduate\u002Fpglist\u002Fphrfbk\u002Fpresentations\u002Fleeds14.pdf)\n\n* [YOUTUBE](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=bD-CWgbsCeI&list=PLgKuh-lKre10UQnP7gBCFoKgq5KWIA7el) - 关于张量网络的一些想法，特别适用于量子算法\n\n\u003Ca name=\"quantumalgorithmsml\">\u003C\u002Fa>\n## 量子机器学习算法\n\n\u003Ca name=\"quantumalgorithmsml-qknn\">\u003C\u002Fa>\n#### 量子K近邻\n \n##### 信息：这里可以使用量子比特两个状态之间的交换门测试来检测质心（欧几里得距离）。由于KNN是回归问题，因此可以使用平均值来计算损失。\n \n* [微软PDF1](https:\u002F\u002Fwww.microsoft.com\u002Fen-us\u002Fresearch\u002Fpublication\u002Fquantum-nearest-neighbor-algorithms-for-machine-learning\u002F) - 理论解释\n* [PDF2](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1409.3097.pdf) - 一本很好的入门材料\n* [Matlab](https:\u002F\u002Fgithub.com\u002Fkrishnakumarsekar\u002F) - 将于近期推出\n* [Python](https:\u002F\u002Fgithub.com\u002Fkrishnakumarsekar\u002F) - 将于近期推出\n \n\u003Ca name=\"quantumalgorithmsml-kmeans\">\u003C\u002Fa>\n#### 量子K均值\n \n##### 信息：有两种可能的方法：1. 使用快速傅里叶变换和逆傅里叶变换构建预言机，计算叠加态的均值；2. 通过绝热哈密顿量生成并求解哈密顿方程来确定聚类中心。\n \n* [PDF1](https:\u002F\u002Fpdfs.semanticscholar.org\u002F6d77\u002F54d33958b4a41d57ec99558eb28ae88f9884.pdf) - 以一种很好的方式将量子K均值应用于图像\n* [PDF2](http:\u002F\u002Fwww.machinelearning.org\u002Fproceedings\u002Ficml2007\u002Fpapers\u002F518.pdf) - 理论\n* [PDF3](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1307.0411.pdf) - 很好地解释了利用哈密顿量进行K均值聚类的方法\n* [Matlab](https:\u002F\u002Fgithub.com\u002Fkrishnakumarsekar\u002F) - 将于近期推出\n* [Python](https:\u002F\u002Fgithub.com\u002Fkrishnakumarsekar\u002F) - 将于近期推出\n \n\u003Ca name=\"quantumalgorithmsml-qfcm\">\u003C\u002Fa>\n#### 量子模糊C均值\n \n##### 信息：与K均值类似，Fuzzy C均值也使用预言机方法，但不同的是，这里不是计算均值，而是通过优化预言机后再应用旋转门，从而获得较好的结果。\n \n* [PDF1](https:\u002F\u002Fpdfs.semanticscholar.org\u002F6d77\u002F54d33958b4a41d57ec99558eb28ae88f9884.pdf) - 理论\n* [Matlab](https:\u002F\u002Fgithub.com\u002Fkrishnakumarsekar\u002F) - 将于近期推出\n* [Python](https:\u002F\u002Fgithub.com\u002Fkrishnakumarsekar\u002F) - 将于近期推出\n \n\u003Ca name=\"quantumalgorithmsml-svm\">\u003C\u002Fa>\n#### 量子支持向量机\n \n##### 信息：与上述算法略有不同，这里的核函数是通过经典方法准备的，而整个训练过程则在预言机中进行，由预言机完成分类。由于SVM是线性模型，因此需要基于最优误差（最小二乘对偶形式的最优解）的回归来提升性能。\n \n* [PDF1](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1307.0471.pdf) - 解释得很好，但稍微有些难懂 :)\n* [PDF2](http:\u002F\u002Fwww.scirp.org\u002Fjournal\u002FPaperInformation.aspx?paperID=72542) - QSVM的良好应用\n* [Matlab](https:\u002F\u002Fgithub.com\u002Fkrishnakumarsekar\u002F) - 将于近期推出\n* [Python](https:\u002F\u002Fgithub.com\u002Fkrishnakumarsekar\u002F) - 将于近期推出\n \n\u003Ca name=\"quantumalgorithmsml-genetic\">\u003C\u002Fa>\n#### 量子遗传算法\n \n##### 信息：这是最适合量子领域的算法之一。在这里，染色体作为量子比特向量，交叉操作通过评估来实现，而突变则通过门的旋转来完成。\n \n[![流程图](https:\u002F\u002Fwww.hindawi.com\u002Fjournals\u002Fmpe\u002F2013\u002F730749.fig.001.jpg)]() \n\n* [PDF1](https:\u002F\u002Fwww.hindawi.com\u002Fjournals\u002Fmpe\u002F2013\u002F730749\u002F) - 非常精彩的文章，解释清晰且内容丰富\n* [PDF2](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1202.2026.pdf) - 涉及大量理论 :)\n* [PDF3](http:\u002F\u002Fccis2k.org\u002Fiajit\u002FPDF\u002Fvol.9,no.3\u002F2107-6.pdf) - 非常出色的比较\n* [Matlab](http:\u002F\u002Fwww.codelooker.com\u002Fid\u002F155\u002F717734.html) - 仿真\n* [Python1](https:\u002F\u002Fgithub.com\u002FResearchCodesHub\u002FQuantumGeneticAlgorithms\u002F) - 仿真\n* [Python2](https:\u002F\u002Fgithub.com\u002Fkrishnakumarsekar\u002F) - 将于近期推出\n \n\u003Ca name=\"quantumalgorithmsml-hmm\">\u003C\u002Fa>\n#### 量子隐马尔可夫模型\n \n##### 信息：由于HMM本身就是基于状态的模型，因此这里的量子态直接作为马尔可夫链中的正常状态，状态之间的转移则通过基于概率分布的量子操作来实现。\n \n[![流程图](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fkrishnakumarsekar_awesome-quantum-machine-learning_readme_bce12b68560b.png)]()\n\n* [PDF1](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1503.08760.pdf) - 好的想法和解释 \n* [PDF2](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1207.4304.pdf) - 不错，但概念略有不同\n* [Matlab](https:\u002F\u002Fgithub.com\u002Fkrishnakumarsekar\u002F) - 尚未实现\n* [Python1](https:\u002F\u002Fgithub.com\u002Fkrishnakumarsekar\u002F) - 尚未实现\n* [Python2](https:\u002F\u002Fgithub.com\u002Fkrishnakumarsekar\u002F) - 尚未实现\n \n\u003Ca name=\"quantumalgorithmsml-bayesian\">\u003C\u002Fa>\n#### 基于贝叶斯方法的量子态分类\n \n##### 信息：量子贝叶斯网络采用与量子态相同的状态概念，但这里的状态分类是为了使训练数据可重用，其依据是状态的密度（干涉）。\n \n[![贝叶斯网络示例1](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fkrishnakumarsekar_awesome-quantum-machine-learning_readme_a51210f223ad.jpg)]() \n[![贝叶斯网络示例2](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fkrishnakumarsekar_awesome-quantum-machine-learning_readme_9987a6dcadf2.jpg)]() \n[![贝叶斯网络示例3](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fkrishnakumarsekar_awesome-quantum-machine-learning_readme_0fb5f46316ce.jpg)]() \n \n* [PDF1](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1204.1550.pdf) - 理论很好 \n* [PDF2](https:\u002F\u002Fwww.ncbi.nlm.nih.gov\u002Fpmc\u002Farticles\u002FPMC4726808\u002F) - 解释得很好\n* [Matlab](https:\u002F\u002Fgithub.com\u002Fkrishnakumarsekar\u002F) - 尚未实现\n* [Python1](https:\u002F\u002Fgithub.com\u002Fkrishnakumarsekar\u002F) - 尚未实现\n* [Python2](https:\u002F\u002Fgithub.com\u002Fkrishnakumarsekar\u002F) - 尚未实现\n \n\u003Ca name=\"quantumalgorithmsml-antcolony\">\u003C\u002Fa>\n#### 量子蚁群优化算法\n \n##### 信息：一种处理多维方程的良好算法。蚁群优化算法最适合解决销售员问题，而量子蚁群优化算法则更适合处理三维或更高维度的销售员问题。在这里，量子旋转电路负责更新信息素，基于量子比特的蚁群在复杂空间中进行全局通信。\n \n[![蚁群优化算法1](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fkrishnakumarsekar_awesome-quantum-machine-learning_readme_61e21d840730.jpg)]() \n \n* [PDF1](http:\u002F\u002Fac.els-cdn.com\u002FS2212667812001359\u002F1-s2.0-S2212667812001359-main.pdf?_tid=42e0cd66-2f4a-11e7-920f-00000aacb361&acdnat=1493738345_8f536599e404c7588811ddd49c484688) - 概念很好 \n* [PDF2](http:\u002F\u002Fwww.sersc.org\u002Fjournals\u002FIJMUE\u002Fvol10_no11_2015\u002F19.pdf) - 应用不错\n* [Matlab](https:\u002F\u002Fgithub.com\u002Fkrishnakumarsekar\u002F) - 尚未实现\n* [Python1](https:\u002F\u002Fgithub.com\u002Fkrishnakumarsekar\u002F) - 尚未实现\n* [Python2](https:\u002F\u002Fgithub.com\u002Fkrishnakumarsekar\u002F) - 尚未实现\n \n\u003Ca name=\"quantumalgorithmsml-caautomata\">\u003C\u002Fa>\n#### 量子细胞自动机\n \n##### 信息：这是一种非常复杂的算法，有多种类型，专门用于多项式方程以及为特定问题设计最优门电路。这里使用量子态形成晶格，并根据两个量子比特之间状态的变化来计算时间。最适合用于纳米电子学领域。\n \n[![量子细胞自动机](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fkrishnakumarsekar_awesome-quantum-machine-learning_readme_fe6b66185175.jpg)]() \n \n* [维基百科](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FQuantum_cellular_automaton) - 基础知识 \n* [PDF1](https:\u002F\u002Farxiv.org\u002Fpdf\u002F0808.0679.pdf) - 仅用于获取关键词\n* [PDF2](http:\u002F\u002Fieee-hpec.org\u002F2013\u002Findex_htm_files\u002F7-Improved-Eigensolver-Baldwin-2867489.pdf) - 解释清晰，应用易于理解 \n* [Matlab](https:\u002F\u002Fgithub.com\u002Fkrishnakumarsekar\u002F) - 尚未实现\n* [Python1](https:\u002F\u002Fgithub.com\u002Fkrishnakumarsekar\u002F) - 尚未实现\n* [Python2](https:\u002F\u002Fgithub.com\u002Fkrishnakumarsekar\u002F) - 尚未实现\n \n \n\u003Ca name=\"qnn\">\u003C\u002Fa>\n\n## 量子神经网络\n \n[![QNN 1](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fkrishnakumarsekar_awesome-quantum-machine-learning_readme_072044ecf6ad.png)](https:\u002F\u002Fsilky.github.io\u002Fposts\u002F2016-12-11-quantum-neural-networks.html)\n\n##### 一句话：这确实是最难理解的主题之一。简单来说，普通神经网络进行的是并行计算，而量子神经网络则是对并行计算的再并行化。理论上，量子神经网络可以结合多种激活函数；但在普通神经网络中，使用超过一种激活函数会降低性能并增加复杂度。\n\n\u003Ca name=\"qnn-perceptron\">\u003C\u002Fa>\n#### 量子感知器\n \n##### 信息：感知器（层）是神经网络的基本单元。量子版本的感知器必须同时解决线性和非线性问题。量子概念结合了线性（叠加原理）和非线性（通过概率近似状态）。要在量子世界中构建感知器，需要将非线性转换到一定限度，而这正是相位估计算法所承担的任务。\n\n[![Quantum Perceptron 1](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fkrishnakumarsekar_awesome-quantum-machine-learning_readme_32c00ed15e82.jpg)](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FActivation_function) \n[![Quantum Perceptron 2](https:\u002F\u002Fwww.nature.com\u002Farticle-assets\u002Fnpg\u002Fsrep\u002F2014\u002F140107\u002Fsrep03589\u002Fimages\u002Fm685\u002Fsrep03589-f2.jpg)](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FQuantum_phase_estimation_algorithm) \n[![Quantum Perceptron 3](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fkrishnakumarsekar_awesome-quantum-machine-learning_readme_250143ef0fc7.png)]()\n[![Quantum Perceptron 4](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fkrishnakumarsekar_awesome-quantum-machine-learning_readme_79cf8276018d.gif)](https:\u002F\u002Fwww.omicsonline.org\u002Fopen-access\u002Fquantum-neural-network-based-parts-of-speech-tagger-for-hindi-0976-4860-5-137-152.pdf.php?aid=35658) \n[![Quantum Perceptron 5](https:\u002F\u002F3c1703fe8d.site.internapcdn.net\u002Fnewman\u002Fcsz\u002Fnews\u002F800\u002F2015\u002Fneuralqubits.jpg)](https:\u002F\u002Fwww.researchgate.net\u002Fpublication\u002F231178445_Quantum_Learning_and_Quantum_Perceptrons) \n\n* [PDF1](https:\u002F\u002Farxiv.org\u002Fpdf\u002Fquant-ph\u002F0201144.pdf) - 良好的理论 \n* [PDF2](http:\u002F\u002Faxon.cs.byu.edu\u002Fpapers\u002Fricks.nips03.pdf\u002F) - 良好的解释\n* [Matlab](https:\u002F\u002Fgithub.com\u002Fkrishnakumarsekar\u002F) - 尚未实现\n* [Python1](https:\u002F\u002Fgithub.com\u002Fkrishnakumarsekar\u002F) - 尚未实现\n* [Python2](https:\u002F\u002Fgithub.com\u002Fkrishnakumarsekar\u002F) - 尚未实现\n \n\u003Ca name=\"quantumstatistics\">\u003C\u002Fa>\n## 量子统计数据分析\n \n[![quantumstatistics1](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fkrishnakumarsekar_awesome-quantum-machine-learning_readme_37703ac8db62.jpg)](https:\u002F\u002Fwww.slideshare.net\u002Ftanafuyu\u002Fslide-2014-rims1031public)\n[![quantumstatistics2](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fkrishnakumarsekar_awesome-quantum-machine-learning_readme_464870c6b232.jpg)](https:\u002F\u002Fwww.slideshare.net\u002Ftanafuyu\u002Fslide-2014-rims1031public)\n[![quantumstatistics3](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fkrishnakumarsekar_awesome-quantum-machine-learning_readme_b3a2d1350af2.jpg)](https:\u002F\u002Fwww.slideshare.net\u002Ftanafuyu\u002Fslide-2014-rims1031public)\n[![quantumstatistics4](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fkrishnakumarsekar_awesome-quantum-machine-learning_readme_0c564a029925.png)](https:\u002F\u002Farxiv.org\u002Fpdf\u002F0802.1296.pdf)\n[![quantumstatistics5](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fkrishnakumarsekar_awesome-quantum-machine-learning_readme_755d97d11f3b.jpg)](https:\u002F\u002Fwww.slideshare.net\u002Fmleifer\u002Fquantum-dynamics-as-generalized-conditional-probabilities)\n[![quantumstatistics6]( https:\u002F\u002Fimage.slidesharecdn.com\u002Fguelph200609-090408123316-phpapp02\u002F95\u002Fquantum-dynamics-as-generalized-conditional-probabilities-4-728.jpg?cb=1239194048)](https:\u002F\u002Fwww.slideshare.net\u002Fmleifer\u002Fconditional-density-operators-in-quantum-information) \n\n##### 一句话：这是一个正在研究的概念，可以从多个角度理解。最好的方式是：如果你想要在当前的经典理论中对一个问题应用高阶导数，由于其串行计算的问题，计算起来非常困难。相反，如果采用并行化的微分方法，你必须通过概率来估计所有路径上的值。量子概率可以帮助实现这一点，因为这样可以大大减少损失。另一种相对较为热门的方法是量子贝叶斯主义，它是一种解决统计学中大多数不确定性问题的方案，能够在高度先进的物理研究中结合时间和空间。\n\n \n\u003Ca name=\"qpl\">\u003C\u002Fa>\n\n## 量子编程语言、工具和软件\n \n\n\u003Ca name=\"qpl-all\">\u003C\u002Fa>\n#### 全部\n \n##### 信息：所有编程语言、软件和工具按字母顺序排列 \n \n* [软件](https:\u002F\u002Fwww.quantiki.org\u002Fwiki\u002Flist-qc-simulators) - 关于所有量子计算模拟器的优秀内容\n* [Python库](http:\u002F\u002Fqutip.org\u002F) - 一个Python库\n* [基于Matlab的Python库](https:\u002F\u002Fpypi.python.org\u002Fpypi\u002Fqit) - Matlab Python库\n* [量子张量网络GitHub](https:\u002F\u002Fgithub.com\u002Femstoudenmire\u002FTNML) - 张量网络\n* [Bayesforge](http:\u002F\u002Fbayesforge.com\u002F) - 一个基于亚马逊云服务的优秀框架，用于量子算法和数据分析\n* [Rigetti](https:\u002F\u002Fgithub.com\u002Frigetticomputing) - 一个用于实时使用量子计算机的最佳工具库\n* [Rigetti Forest](http:\u002F\u002Fwww.rigetti.com\u002Findex.php\u002Fforest) - 连接量子计算机的API\n* [quil\u002FpyQuil](http:\u002F\u002Fpyquil.readthedocs.io\u002Fen\u002Flatest\u002Foverview.html) - 一种用于Forest框架的量子指令语言\n* [Grove](https:\u002F\u002Fgithub.com\u002Frigetticomputing\u002Fgrove) - Grove是一个展示量子傅里叶变换、相位估计、量子近似优化算法等项目的仓库，这些项目都是使用Forest开发的。\n* [QISKit](https:\u002F\u002Fgithub.com\u002FQISKit) - IBM提供的访问量子计算机的工具包，主要用于量子电路\n* [IBM Bluemix模拟器](https:\u002F\u002Fquantumexperience.ng.bluemix.net\u002Fqx\u002Feditor) - 用于量子电路的Bluemix模拟器\n* [微软量子开发工具包](https:\u002F\u002Fmarketplace.visualstudio.com\u002Fitems?itemName=quantum.DevKit) - 微软Visual Studio支持的量子电路创建工具包\n* [微软“Q#”](https:\u002F\u002Fdocs.microsoft.com\u002Fen-us\u002Fquantum\u002Fquantum-WriteAQuantumProgram?view=qsharp-preview) - 微软推出的新型编程语言Q#，用于量子电路的编写\n* [qiskit api python](https:\u002F\u002Fgithub.com\u002FQISKit\u002Fqiskit-api-py) - 一个连接IBM量子计算机的API。通过生成的令牌可以轻松连接，但目前可用的工具还非常有限，未来将推出大量新工具。\n* [Cyclops张量框架](http:\u002F\u002Fsolomon2.web.engr.illinois.edu\u002Fctf\u002F) - 一个用于张量网络模拟的框架\n* [用于化学和物理量子算法模拟的Python工具包](https:\u002F\u002Fgithub.com\u002Fqmlcode\u002Fqml) - 一个新启动的项目，用于模拟分子和固体\n* [基于贝叶斯方法的量子项目库](https:\u002F\u002Fgithub.com\u002Fartiste-qb-net) - 一个优秀的代码库，也是Bayesforge的起点\n* [谷歌费米子产品](https:\u002F\u002Fgithub.com\u002Fquantumlib) - 一款新推出的专门用于化学模拟的产品\n* [树状张量网络](https:\u002F\u002Fgithub.com\u002Fdingliu0305\u002FTree-Tensor-Networks-in-Machine-Learning) - 孵化中的有趣张量网络\n* [深度张量神经网络](https:\u002F\u002Fgithub.com\u002Fatomistic-machine-learning\u002Fdtnn) - 关于孵化中张量神经网络的一些有用信息\n* [生成式张量网络](http:\u002F\u002Fgtn.ai\u002F) - 一家初创公司，利用张量网络进行机器学习，应用于药物发现\n* [谷歌Bristlecone](https:\u002F\u002Fresearch.googleblog.com\u002F2018\u002F03\u002Fa-preview-of-bristlecone-googles-new.html) - 谷歌推出的一款新型量子处理器，旨在为未来的硬件提供全面的人工智能支持。\n* [XANADU](https:\u002F\u002Fwww.xanadu.ai\u002F) - 一家专注于光量子硬件（芯片支持）及软件的公司，目前处于筹备阶段。不久将进入市场。\n* [fathom computing](https:\u002F\u002Fwww.fathomcomputing.com\u002F) - 一种利用光和量子概念在处理器中训练人工智能的新理念。相关产品即将发布。\n* [阿里巴巴量子计算云服务](https:\u002F\u002Fwww.alibabacloud.com\u002Fpress-room\u002Falibaba-cloud-and-cas-launch-one-of-the-worlds-most) - 提供访问11比特量子计算处理器的云服务\n* [原子级机器学习项目](https:\u002F\u002Fgithub.com\u002Fatomistic-machine-learning) - 看起来与用于量子化学应用的深度张量网络有关，值得关注。\n* [circQ与谷歌的工作](https:\u002F\u002Fai.google\u002Fresearch\u002Fteams\u002Fapplied-science\u002Fquantum\u002F) - 谷歌在工具方面的顶级努力\n* [IBM云端安全密码学](https:\u002F\u002Fwww.sdxcentral.com\u002Farticles\u002Fnews\u002Fibm-drives-quantum-safe-cryptography-into-its-public-cloud\u002F2019\u002F08\u002F) - IBM已开始并正在开发一种量子安全密码学技术，计划通过云服务取代现有的所有证书颁发机构。\n* [谷歌张量网络开源](https:\u002F\u002Fai.googleblog.com\u002F2019\u002F06\u002Fintroducing-tensornetwork-open-source.html) - 谷歌推出了科学家们最青睐的量子计算机使用方式——张量流。它能够简化网络设计，并自动处理门操作、处理器准备等工作，同时还能展现数学之美。\n* [谷歌张量网络GitHub](https:\u002F\u002Fgithub.com\u002Fgoogle\u002FTensorNetwork) - 谷歌张量网络的GitHub项目\n* [量子TensorFlow](https:\u002F\u002Fgithub.com\u002Fkrishnakumarsekar\u002F) - 尚未推出\n* [量子Spark](https:\u002F\u002Fgithub.com\u002Fkrishnakumarsekar\u002F) - 尚未推出\n* [量子Map Reduce](https:\u002F\u002Fgithub.com\u002Fkrishnakumarsekar\u002F) - 尚未推出\n* [量子数据库](https:\u002F\u002Fgithub.com\u002Fkrishnakumarsekar\u002F) - 尚未推出\n* [量子服务器](https:\u002F\u002Fgithub.com\u002Fkrishnakumarsekar\u002F) - 尚未推出\n* [量子数据分析](https:\u002F\u002Fgithub.com\u002Fkrishnakumarsekar\u002F) - 尚未推出\n \n\n\u003Ca name=\"quantumhottopics\">\u003C\u002Fa>\n\n## 量子热点话题\n \n\n\u003Ca name=\"quantumhottopics-deepquantumlearning\">\u003C\u002Fa>\n#### 深度量子学习\n \n##### 为什么以及什么是深度学习？\n###### 简而言之，如果你掌握了深度学习，就能找到一份好工作 :)。即使是来自不同专业背景的本科生和研究生，只要在深度学习领域完成硕士专业课程，也能在这个庞大的行业中工作 :)。从实践角度来看，机器学习（向量数学）、深度学习（向量空间（图形）数学）和大数据这些术语，其实是大型公司为了在市场上制造潮流而创造出来的概念。但在科学和研究领域，并没有这样的严格定义。如今，如果你问一家大公司里刚入职的年轻人“什么是深度学习”，他们可能会回答：“就是用卷积神经网络对无监督数据进行随机梯度下降的线性回归而已 :)”。他们清楚地知道这些术语的含义，也知道如何用这些技术对一堆“相对数据”进行编程。但如果你再问他们关于FCM、SVM和HMM等算法，他们通常会说这些都是老派算法，已经被深度学习取代了 :)。然而实际上，他们并不了解这些算法和数学理论从诞生到当前水平的发展历程及其有效性；也不知道为实现这种“隐藏复杂性的技术”，究竟有多少关于向量、空间、张量等方面的数学定理被证明。他们从未真正处理过像医学影像、天文图像、地质图像等非相对数据，因为在这些数据中寻找关联和特征是非常复杂的，而要对大量图像进行循环匹配更是巨大的工程。因此，如今所谓的深度学习（即多层人工神经网络）并不适合解决这类问题。\n\n##### 为什么需要量子深度学习或深度量子学习？\n###### 在人工神经网络研究的中期，人们意识到人工神经网络所能执行的数学运算存在极限，而其设计目标正是实现多种数学运算的并行化。在人工智能领域，所谓“世界智能”本质上就是数学能力——一个问题能否被有效解决，取决于应用于该问题的数学逻辑。逻辑越强大，系统的性能就越出色（也就越“智能”）。这一目标为量子人工神经网络的研究打开了大门。将深度学习的思想引入量子力学环境后，便有可能对大量非相关数据应用复杂的数学方程，从而发现更多特征并提升系统性能。\n \n\u003Ca name=\"qmlvsdl\">\u003C\u002Fa>\n## 量子机器学习与深度学习\n \n##### 这是一个很有趣的话题。近年来，许多产品型公司（如谷歌、微软等）的员工都在使用“深度学习”这个词。那么，深度学习到底是什么？它真的是一个全新的发明吗？应该如何学习它？它是否正在取代机器学习？这些问题常常困扰着初级研究人员和中层员工。\n \n##### 对于所有这些问题，有一个简单的答案：深度学习就是一系列并行的“for”循环，仅此而已。这是一种高效地重复执行多项任务、降低计算成本的方式。然而，这种方式也在数学和计算机科学之间划出了一道鸿沟。这是为什么呢？\n\n##### 所有经典的算法都是基于串行处理的，它们依赖于前一次循环的反馈结果。如果将一个串行的经典算法直接应用到多个集群上，并不会得到理想的效果。不过，一些轻量级的并行经典算法（即深度学习）可以在多个集群上完成任务，但对于复杂问题仍然不够适用。那么，该如何解决这个问题呢？\n\n##### 正如标题所言，量子机器学习的优势在于，深度学习只是简单地对数据进行批量处理，而量子机器学习则可以根据算法的要求来安排批量处理的方式。\n\n##### 许多科技公司已经意识到了这一点，并开始转向量子机器学习。在量子计算框架下运行经典算法，往往比在传统计算机上运行深度学习算法效果更好。未来的目标是将两者结合起来，以实现更加卓越的结果。\n\n##### 参考资料\n \n* [Quora](https:\u002F\u002Fwww.quora.com\u002FHow-will-quantum-computing-revolutionize-deep-learning) - 一场精彩的讨论\n* [Quora](https:\u002F\u002Fwww.quora.com\u002FWill-quantum-computing-change-machine-learning) - 关于两者的桥梁式讨论\n* [Pdf](http:\u002F\u002Fwww.scottaaronson.com\u002Fpapers\u002Fqml.pdf) - 一篇不错的讨论文章\n* [Google](https:\u002F\u002Fventurebeat.com\u002F2015\u002F11\u002F11\u002Fgoogle-researcher-quantum-computers-arent-perfect-for-deep-learning\u002F) - 谷歌研究人员的观点\n* [Microsoft](http:\u002F\u002Fwww.physics.usyd.edu.au\u002Fquantum\u002FCoogee2015\u002FPresentations\u002FSvore.pdf) - 微软计划将两者融合\n* [IBM](https:\u002F\u002Fwww.rtinsights.com\u002Fibm-quantum-computing-with-machine-learning-in-cloud\u002F) - IBM计划将两者结合\n* [IBM项目](https:\u002F\u002Fwww.ibm.com\u002Fblogs\u002Fresearch\u002F2017\u002F03\u002Fquantum-algorithm-classifies-9500-handwritten-numbers\u002F) - IBM的一个项目构想\n* [MIT和谷歌](https:\u002F\u002Fwww.technologyreview.com\u002Fs\u002F544421\u002Fgoogles-quantum-dream-machine\u002F) - 对所有问题的解答\n \n\n\u003Ca name=\"quantummeetups\">\u003C\u002Fa>\n\n## 量子聚会\n\n* [聚会1](https:\u002F\u002Fwww.meetup.com\u002FQuantum-Physics-Drinks\u002F) - 量子物理\n* [聚会2](https:\u002F\u002Fwww.meetup.com\u002FLondon-Quantum-Computing-Meetup\u002F) - 伦敦量子计算\n* [聚会3](https:\u002F\u002Fwww.meetup.com\u002FNew-York-Quantum-Computing-Meetup\u002F) - 纽约量子计算\n* [聚会4](https:\u002F\u002Fwww.meetup.com\u002FQuantum-Computing-and-Big-Data\u002Fevents\u002F238749477\u002F) - 加拿大量子计算\n* [聚会5](https:\u002F\u002Fwww.meetup.com\u002FAustin-Quantum-Computing-Artificial-Intelligence-Meetup\u002F) - 德克萨斯州量子人工智能\n* [聚会6](https:\u002F\u002Fwww.meetup.com\u002FThe-NY-Quantum-Theory-Group\u002F) - 纽约一般量子力学、数学\n* [聚会7](https:\u002F\u002Fwww.meetup.com\u002FQuantum-Computers\u002F) - 加州山景城量子计算\n* [聚会8](https:\u002F\u002Fwww.meetup.com\u002Fnyhackr\u002F) - 纽约统计分析\n* [聚会9](https:\u002F\u002Fwww.meetup.com\u002FQuantum-Physics-Meetup-Group\u002F) - 英国伦敦量子力学\n* [聚会10](https:\u002F\u002Fwww.meetup.com\u002FQuantum-Physics-Drinks\u002F) - 澳大利亚悉尼量子物理\n* [聚会11](https:\u002F\u002Fwww.meetup.com\u002FBerkeley-Quantum-Physics-Spirituality-Meetup\u002F) - 加州伯克利量子物理\n* [聚会12](https:\u002F\u002Fwww.meetup.com\u002FQuantumX-Quantum-Computing-Meetup\u002F) - 英国伦敦量子计算\n* [聚会13](https:\u002F\u002Fwww.meetup.com\u002FCarmichael-Quantum-Christians\u002F) - 加州卡迈克尔量子力学\n* [聚会14](https:\u002F\u002Fwww.meetup.com\u002FRelativity-Exploration-of-Portland\u002F) - 波特兰数学与科学小组\n* [聚会15](https:\u002F\u002Fwww.meetup.com\u002FQuantum-Physics-Discussion-Group\u002F) - 加州圣莫尼卡量子物理\n* [聚会16](https:\u002F\u002Fwww.meetup.com\u002FQuantum-Vibrational-Healing\u002F) - 伦敦量子力学\n* [聚会17](https:\u002F\u002Fwww.meetup.com\u002FLondon-Quantum-Computing-Meetup\u002F) - 伦敦量子计算\n* [聚会18](https:\u002F\u002Fwww.meetup.com\u002Fquantum-metaphysics\u002F) - 美国密苏里州堪萨斯城量子形而上学\n* [聚会19](https:\u002F\u002Fwww.meetup.com\u002FQuantum-Content\u002F) - 美国马萨诸塞州波士顿量子力学与物理学\n* [聚会20](https:\u002F\u002Fwww.meetup.com\u002FQuantum-Organization\u002F) - 美国加利福尼亚州旧金山量子物理与力学\n* [聚会21](https:\u002F\u002Fwww.meetup.com\u002FTheoretical-Quantum-Mechanics\u002F) - 美国宾夕法尼亚州兰霍恩量子力学\n* [聚会22](https:\u002F\u002Fwww.meetup.com\u002FPortland-Science-Meetup\u002F) - 波特兰量子力学\n\n \n\u003Ca name=\"quantumdegrees\">\u003C\u002Fa>\n## 基于量子的学位课程\n\n##### 全球各地有许多相关课程，且越来越多的大学正在陆续开设。与其只涵盖量子机器学习，不如全面覆盖所有量子相关主题，以下列出了一些可供选择的课程：\n \n#### 可用课程\n\n###### 科学与工程领域的量子力学\n\n* 在线课程\n \n\t* [斯坦福大学](http:\u002F\u002Fonline.stanford.edu\u002Fcourse\u002Fqmse01-quantum-mechanics-scientists-and-engineers) - 优秀的预备课程\n\t* [edX](https:\u002F\u002Fcourses.edx.org\u002Fcourses\u002Fcourse-v1:GeorgetownX+PHYX-008-01x+1T2017\u002Finfo) - 面向大众的量子力学\n * [NPTEL 1](http:\u002F\u002Fnptel.ac.in\u002Fcourses\u002F115104096\u002F) - 一系列优秀的课程，帮助理解量子力学的基础和核心内容\n * [NPTEL 2](http:\u002F\u002Fnptel.ac.in\u002Fcourses\u002F115102023\u002F)\n * [NPTEL 3](http:\u002F\u002Fnptel.ac.in\u002Fcourses\u002F115106066\u002F)\n * [NPTEL 4](http:\u002F\u002Fnptel.ac.in\u002Fcourses\u002F115108074\u002F)\n * [NPTEL 5](http:\u002F\u002Fnptel.ac.in\u002Fcourses\u002F115101010\u002F)\n \n* 线下课程\n \n\t* 英国\n\n\t\t* [布里斯托尔大学](http:\u002F\u002Fwww.bristol.ac.uk\u002Fmaths\u002Fstudy\u002Fundergraduate\u002Funits1617\u002Flevelh6units\u002Fquantum-mechanics-math35500\u002F)\n \n\t* 澳大利亚\n \n\t\t* [澳大利亚国立大学](http:\u002F\u002Fprogramsandcourses.anu.edu.au\u002Fcourse\u002FPHYS2013)\n \n\t* 欧洲\n \n\t\t* [马克斯·普朗克大学](http:\u002F\u002Fprogramsandcourses.anu.edu.au\u002Fcourse\u002FPHYS2013)\n \n###### 量子物理学\n \n* 在线课程\n\n\t* [麻省理工学院](https:\u002F\u002Focw.mit.edu\u002Fcourses\u002Fphysics\u002F8-04-quantum-physics-i-spring-2013\u002Flecture-videos\u002F) - 讲解非常透彻，基础扎实\n * [NPTEL](http:\u002F\u002Fnptel.ac.in\u002Fcourses\u002F122106034\u002F) - 一系列优秀的课程，帮助理解量子物理的基础和核心内容\n\n* 线下课程\n \n\t* 欧洲\n\n\t\t* [哥本哈根大学](http:\u002F\u002Fwww.nbi.ku.dk\u002Fenglish\u002Fresearch\u002Fquantum-physics\u002F)\n \n###### 量子化学\n\n* 在线课程\n\n * [NPTEL 1](http:\u002F\u002Fnptel.ac.in\u002Fcourses\u002F104108057\u002F) - 一系列优秀的课程，帮助理解量子化学的基础和核心内容\n * [NPTEL 2](http:\u002F\u002Fnptel.ac.in\u002Fcourses\u002F104106083\u002F) - \n\n* 线下课程\n \n\t* 欧洲\n\n\t\t* [比利时根特大学](http:\u002F\u002Fwww.quantum.ugent.be\u002F)\n \n###### 量子计算\n\n* 在线课程\n\n\t* [麻省理工学院](https:\u002F\u002Focw.mit.edu\u002Fcourses\u002Fmathematics\u002F18-435j-quantum-computation-fall-2003\u002Findex.htm) - 讲解深入，基础扎实\n\t* [edX](https:\u002F\u002Fwww.edx.org\u002Fcourse\u002Fquantum-mechanics-quantum-computation-uc-berkeleyx-cs-191x) - 讲解清晰易懂\n * [NPTEL](http:\u002F\u002Fnptel.ac.in\u002Fcourses\u002F104104082\u002F) - 一系列优秀的课程，帮助理解量子计算的基础和核心内容\n\n* 线下课程\n \n\t* 加拿大\n \n\t\t* [滑铁卢大学](https:\u002F\u002Fuwaterloo.ca\u002Finstitute-for-quantum-computing\u002F)\n\n\t* 新加坡\n \n\t\t* [新加坡国立大学](http:\u002F\u002Fwww.quantumlah.org\u002F)\n\n\t* 美国\n \n\t\t* [伯克利](http:\u002F\u002Fwww.quantlahoma.org\u002F)\n \n * 中国\n \n * [百度](https:\u002F\u002Fmedium.com\u002F@Synced\u002Fbaidu-launches-institute-of-quantum-computing-899454cbe1c5)\n \n###### 量子技术\n\n* 线下课程\n \n\t* 加拿大\n \n\t\t* [滑铁卢大学](https:\u002F\u002Fuwaterloo.ca\u002Finstitute-for-quantum-computing\u002F)\n\n* 新加坡\n \n\t\t* [新加坡国立大学](http:\u002F\u002Fwww.quantumlah.org\u002F)\n\n\t* 欧洲\n \n\t\t* [慕尼黑](http:\u002F\u002Fwww.munich-quantum-center.de\u002Findex.php?id=1)\n \n * 俄罗斯\n \n * [斯科尔科沃科技大学](http:\u002F\u002Fcrei.skoltech.ru\u002Fcpqm)\n \n \n###### 量子信息科学\n\n* 外部链接\n\n\t* [quantwiki](https:\u002F\u002Fwww.quantiki.org\u002Fwiki\u002Fcourses-quantum-information-science)\n \n* 在线资源\n\n\t* [麻省理工学院](https:\u002F\u002Focw.mit.edu\u002Fcourses\u002Fmedia-arts-and-sciences\u002Fmas-865j-quantum-information-science-spring-2006\u002F) - 非常详细的讲解，基础知识扎实\n\t* [edx](https:\u002F\u002Fwww.edx.org\u002Fcourse\u002Fquantum-information-science-ii-mitx-8-371x) - 讲解清晰易懂\n * [NPTEL](http:\u002F\u002Fnptel.ac.in\u002Fcourses\u002F115101092\u002F) - 一系列优秀的课程，帮助理解量子信息与计算的基础和核心内容\n\n* 课堂课程\n \n\t* 美国\n \n\t\t* [麻省理工学院](http:\u002F\u002Fqis.mit.edu\u002F)\n\t\t* [斯坦福大学](https:\u002F\u002Fweb.stanford.edu\u002Fgroup\u002Fyamamotogroup\u002F)\n \t* [马里兰大学量子信息与计算机科学联合中心](http:\u002F\u002Fquics.umd.edu\u002F)\n \n\t* 加拿大\n \n\t\t* [圆周理论物理研究所](https:\u002F\u002Fperimeterinstitute.ca\u002Fresearch\u002Fresearch-areas\u002Fquantum-information)\n\n\t* 新加坡\n \n\t\t* [新加坡国立大学](http:\u002F\u002Fwww.quantlahoma.org\u002F)\n\n\t* 欧洲\n \n\t\t* [比利时布鲁塞尔自由大学](http:\u002F\u002Fquic.ulb.ac.be\u002Fteaching)\n * [奥地利维也纳量子光学与量子信息研究所](https:\u002F\u002Fiqoqi.at\u002Fen)\n \n \n###### 量子电子学\n \n* 在线资源\n\n * [麻省理工学院](https:\u002F\u002Focw.mit.edu\u002Fcourses\u002Felectrical-engineering-and-computer-science\u002F6-974-fundamentals-of-photonics-quantum-electronics-spring-2006\u002F) - 非常精彩的课程\n * [NPTEL](http:\u002F\u002Fnptel.ac.in\u002Fcourses\u002F115102022\u002F) - 一系列优秀的课程，帮助理解量子电子学的基础和核心内容\n\n* 课堂课程\n \n * 美国\n \n\t\t* [德克萨斯大学](http:\u002F\u002Fwww.ece.utexas.edu\u002Fresearch\u002Fareas\u002Fplasma-quantum-electronics-and-optics) \n \n\t* 欧洲\n \n\t\t* [苏黎世联邦理工学院](http:\u002F\u002Fwww.iqe.phys.ethz.ch\u002Futils\u002Fcontact.html)\n * [巴塞罗那光子科学研究所](http:\u002F\u002Fquantumtech.icfo.eu\u002F) \n \n\t* 亚洲\n \n\t\t* [塔塔基础研究学院](http:\u002F\u002Fwww.tifr.res.in\u002F~quantro\u002Findex.html)\n \n###### 量子场论\n\n* 在线资源\n \n\t* [斯坦福大学](https:\u002F\u002Focw.mit.edu\u002Fcourses\u002Fphysics\u002F8-323-relativistic-quantum-field-theory-i-spring-2008\u002F) - 优秀的预备课程\n * [edx](https:\u002F\u002Fwww.edx.org\u002Fcourse\u002Feffective-field-theory-mitx-8-eftx) - 提供了一些量子场论的概念\n \n* 课堂课程\n \n\t* 英国\n\n\t\t* [帝国理工学院](http:\u002F\u002Fwww.imperial.ac.uk\u002Ftheoretical-physics\u002Fpostgraduate-study\u002Fmsc-in-quantum-fields-and-fundamental-forces\u002F)\n \n\t* 欧洲\n \n\t\t* [布鲁塞尔自由大学](http:\u002F\u002Fwww.vub.ac.be\u002Fen\u002Fstudy\u002Ffiches\u002F56659\u002Fquantum-field-theory)\n \n###### 量子计算机科学\n \n* 课堂课程\n \n\t* 美国\n\n\t\t* [牛津大学](https:\u002F\u002Fwww.cs.ox.ac.uk\u002Fteaching\u002Fcourses\u002Fquantum\u002F)\n * [马里兰大学量子信息与计算机科学联合中心](http:\u002F\u002Fquics.umd.edu\u002F)\n \n###### 量子人工智能与机器学习\n\n* 外部链接\n\n\t* [Quora 1](https:\u002F\u002Fwww.quora.com\u002FQuantum-Computing-vs-Artificial-Intelligence-for-a-PhD)\n * [Quora 1](https:\u002F\u002Fwww.quora.com\u002FWhere-can-you-get-a-PhD-in-quantum-machine-learning)\n * [因斯布鲁克大学关于量子设计的人工智能研究](https:\u002F\u002Fwww.uibk.ac.at\u002Fnewsroom\u002Fartificial-agent-designs-quantum-experiments.html.en)\n \n###### 量子数学\n\n* 课堂课程\n \n\t* 美国\n\n\t\t* [圣母大学 ***](http:\u002F\u002Facms.nd.edu\u002Fresearch\u002F)\n\n\n\u003Ca name=\"quantumconsolidatedresearchpapers\">\u003C\u002Fa>\n\n\n## 整合后的量子研究论文\n\n* [scirate](https:\u002F\u002Fscirate.com\u002F) - 拥有大量量子研究论文\n* [彼得·维特克](http:\u002F\u002Fpeterwittek.com\u002Fbook.html) - 著名的量子机器学习研究者，出版了相关书籍\n* [穆菲·叶振牛] (https:\u002F\u002Fscholar.google.com\u002Fcitations?user=0wJPxfkAAAAJ&hl=en) - 一位优秀的研究人员，发表了许多高质量的文章\n\n\u003Ca name=\"quantumconsolidatedresearchpapers\">\u003C\u002Fa>\n\n## 最近的量子更新论坛、页面和通讯\n\n* [Quantum-Tech](https:\u002F\u002Fmedium.com\u002Fquantum-tech) - 一个精美的通讯页面，发布了许多精彩的链接\n* [Facebook量子机器学习](https:\u002F\u002Fwww.facebook.com\u002Fquantummachinelearning) - 由我运营。内容不算特别好 :)。不过你可以从中获得一些灵感\n* [LinkedIn量子机器学习](https:\u002F\u002Fwww.linkedin.com\u002Fgroups\u002F8592758) - 一个由专家运营的好页面。可以获取大量想法\n* [FOSDEM 2019量子技术演讲](https:\u002F\u002Ffosdem.org\u002F2019\u002Fschedule\u002Ftrack\u002Fquantum_computing\u002F) - FOSDEM 2019为期一天的会议，包含超过10个研究主题、工具和思想\n* [FOSDEM 2020量子技术演讲](https:\u002F\u002Ffosdem.org\u002F2020\u002Fschedule\u002Ftrack\u002Fquantum_computing\u002F) - FOSDEM 2020的现场演讲，提供了许多新的研究主题、工具和想法\n\n### 许可证\n\n[![许可证](http:\u002F\u002Fmirrors.creativecommons.org\u002Fpresskit\u002Fbuttons\u002F88x31\u002Fsvg\u002Fcc-zero.svg)](https:\u002F\u002Fgithub.com\u002Fkrishnakumarsekar\u002Fawesome-quantum-machine-learning\u002Fblob\u002Fmaster\u002FLICENCE)\n\n### 专用开源资源\n\n[![专用开源资源](http:\u002F\u002Flivingintown.com\u002Fwp-content\u002Fuploads\u002Fsites\u002F1112\u002F2015\u002F03\u002Fcoming-soon-small.jpg)]()\n \n* 包含大量图像处理、数据挖掘等领域算法的源代码，支持Matlab、Python、Java和VC++脚本\n* 对众多算法进行了详细解释，并配有流程图等说明\n* 提供了多种算法的对比矩阵\n* [量子机器学习会揭示占星术背后的秘密数学吗？](https:\u002F\u002Fmedium.com\u002F@krishnakumar070891\u002Fis-quantum-machine-learning-will-reveal-the-secret-maths-behind-astrology-ce69fd71a019)\n* 优秀的机器学习和深度学习数学知识已[在线](https:\u002F\u002Fgithub.com\u002Fkrishnakumarsekar\u002Fawesome-machine-learning-deep-learning-mathematics)\n* 发布了量子机器学习系列的基础演示文稿\n[![PPT基础](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fkrishnakumarsekar_awesome-quantum-machine-learning_readme_6ded15b48879.jpg)](https:\u002F\u002Fdocs.google.com\u002Fpresentation\u002Fd\u002F1sqQu3LhX97OIwIEEvDMpzQRh6x52C9XDs1RkbPBM9uM\u002Fpresent)\n[![PPT基础2](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fkrishnakumarsekar_awesome-quantum-machine-learning_readme_3ac8cbac38e0.jpg)](https:\u002F\u002Fdocs.google.com\u002Fpresentation\u002Fd\u002F1TBmkOkfeIifT73p2ENtnU75JgzMXqj9sOPws378-DPc\u002Fpresent)\n\n### 贡献\n\n* 如果你觉得这个页面对你有帮助，请为世界教育慈善机构或渴望学习的孩子们提供支持 \n\u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fkrishnakumarsekar\u002Fawesome-quantum-machine-learning\u002Fblob\u002Fmaster\u002Fcontribution.md\">\u003Cimg src=\"http:\u002F\u002Fcomps.canstockphoto.com\u002Fcan-stock-photo_csp23653568.jpg\" align=\"left\" height=\"200\" width=\"200\">\u003C\u002Fa>","# Awesome Quantum Machine Learning 快速上手指南\n\n`awesome-quantum-machine-learning` 并非一个可直接安装的单一软件库或框架，而是一个**精选资源列表**。它汇集了量子机器学习（QML）领域的算法、学习资料、开源库、软件工具及研究论文。\n\n本指南旨在帮助开发者利用该列表中的资源，快速搭建量子机器学习开发环境并运行第一个示例。\n\n## 环境准备\n\n由于量子计算目前主要处于模拟阶段或依赖云端真机，开发环境通常由经典计算机（用于编写代码）和量子后端（模拟器或云量子计算机）组成。\n\n### 系统要求\n- **操作系统**: Windows 10\u002F11, macOS, 或 Linux (推荐 Ubuntu 20.04+)\n- **内存**: 至少 8GB RAM（运行大型量子电路模拟建议 16GB+）\n- **网络**: 稳定的互联网连接（用于访问云端量子后端如 IBM Quantum, Amazon Braket 等）\n\n### 前置依赖\n在开始之前，请确保已安装以下基础工具：\n- **Python**: 版本 3.8 或更高\n- **Git**: 用于克隆资源仓库\n- **包管理器**: `pip` 或 `conda`\n\n## 安装步骤\n\n由于本项目是资源索引，你需要根据需求安装具体的量子开发框架。目前主流且对中文开发者友好的框架包括 **Qiskit** (IBM) 和 **PennyLane** (Xanadu)。\n\n### 1. 获取资源列表\n首先克隆该仓库以浏览完整的算法实现和研究资料：\n\n```bash\ngit clone https:\u002F\u002Fgithub.com\u002Fkrishnakumarsekar\u002Fawesome-quantum-machine-learning.git\ncd awesome-quantum-machine-learning\n```\n\n### 2. 安装主流量子机器学习框架\n推荐使用 `conda` 创建独立环境以避免依赖冲突。以下以安装 **PennyLane**（专为量子机器学习设计）为例，它支持多种后端插件：\n\n```bash\n# 创建虚拟环境\nconda create -n qml-env python=3.9 -y\nconda activate qml-env\n\n# 安装 PennyLane 核心库\npip install pennylane\n\n# 安装默认量子设备插件 (包含轻量级模拟器)\npip install pennylane-lightning\n\n# (可选) 若需使用 PyTorch 或 TensorFlow 进行混合量子 - 经典训练\npip install torch torchvision\n# 或\npip install tensorflow\n```\n\n> **国内加速提示**: 如果下载速度较慢，建议使用清华或阿里镜像源：\n> ```bash\n> pip install -i https:\u002F\u002Fpypi.tuna.tsinghua.edu.cn\u002Fsimple pennylane pennylane-lightning torch\n> ```\n\n### 3. 配置云端后端（可选）\n若需使用真实量子计算机，需注册相应平台并配置 API Token：\n- **IBM Quantum**: 安装 `qiskit-ibm-runtime` 并配置 `QISKIT_IBM_TOKEN`。\n- **Amazon Braket**: 配置 AWS CLI 凭证。\n\n## 基本使用\n\n以下示例展示如何使用 Python 和 PennyLane 构建并训练一个简单的**变分量子分类器**（对应列表中 \"Quantum Machine Learning Algorithms\" 部分的概念）。\n\n### 示例：量子二分类任务\n\n创建一个名为 `qml_demo.py` 的文件：\n\n```python\nimport pennylane as qml\nfrom pennylane import numpy as np\nimport matplotlib.pyplot as plt\n\n# 1. 定义量子设备 (使用本地模拟器)\ndev = qml.device(\"default.qubit\", wires=2)\n\n# 2. 定义量子节点 (Quantum Node)\n@qml.qnode(dev)\ndef quantum_circuit(weights, x):\n # 编码输入数据 (Angle Encoding)\n qml.RY(x[0], wires=0)\n qml.RY(x[1], wires=1)\n \n # 变分层 (Variational Layer)\n qml.CNOT(wires=[0, 1])\n qml.RY(weights[0], wires=0)\n qml.RY(weights[1], wires=1)\n \n # 返回期望值作为模型输出\n return qml.expval(qml.PauliZ(0))\n\n# 3. 定义损失函数和优化器\ndef cost_function(weights, X, Y):\n predictions = [quantum_circuit(weights, x) for x in X]\n # 均方误差损失\n return np.mean((np.array(predictions) - Y) ** 2)\n\n# 4. 准备简单数据集\nX_data = np.array([[0.1, 0.2], [0.5, 0.6], [0.9, 0.1]])\nY_data = np.array([1, -1, 1])\n\n# 初始化参数\nweights = np.random.normal(0, 1, size=2)\nopt = qml.GradientDescentOptimizer(stepsize=0.1)\n\n# 5. 训练循环\nsteps = 50\nfor i in range(steps):\n weights, loss = opt.step(lambda w: cost_function(w, X_data, Y_data), weights)\n if (i + 1) % 10 == 0:\n print(f\"Step {i+1}: Loss = {loss:.4f}\")\n\nprint(\"\\n训练完成！最终权重:\", weights)\n```\n\n### 运行代码\n\n在终端执行：\n\n```bash\npython qml_demo.py\n```\n\n### 下一步探索\n参考克隆下来的 `awesome-quantum-machine-learning` 目录结构，深入阅读以下章节对应的源码和论文：\n- **QUANTUM ALGORITHMS**: 查看 Grover, Shor 等经典算法的实现。\n- **QAUNTUM NEURAL NETWORK**: 探索量子感知机 (Quantum Perceptrons) 和量子自动编码器。\n- **QUANTUM PROGRAMMING LANGUAGES**: 对比 Qiskit, Cirq, Forest 等不同语言的特性。","某金融科技公司的算法团队正尝试利用量子计算优化高频交易中的投资组合预测模型，但团队成员普遍缺乏量子力学与机器学习交叉领域的系统知识。\n\n### 没有 awesome-quantum-machine-learning 时\n- **入门门槛极高**：工程师需要在 arXiv、GitHub 和各大学术网站间盲目搜索，难以区分哪些是基础概念（如量子叠加、纠缠），哪些是前沿算法，导致学习路径混乱。\n- **理论到实践断层**：即使理解了量子傅里叶变换或变分量子本征求解器（VQE）的数学原理，也找不到对应的代码库或具体项目案例来验证想法。\n- **概念映射困难**：无法快速厘清经典机器学习中的张量、梯度下降如何映射到量子希尔伯特空间或量子线路中，浪费大量时间查阅零散的物理论文。\n- **资源验证成本高**：网上资料质量参差不齐，团队需花费数周时间甄别哪些开源库是维护良好的，哪些已过时，严重拖慢研发进度。\n\n### 使用 awesome-quantum-machine-learning 后\n- **构建清晰学习路径**：团队直接利用其整理的“基础知识”和“量子计算桥接”章节，迅速掌握了从量子比特编码到量子线路设计的核心概念，统一了团队认知。\n- **快速获取落地参考**：通过\"Quantum Algorithms\"和\"Projects\"板块，直接找到了 VQE 和量子核方法的成熟实现库与案例描述，将算法验证周期从数周缩短至几天。\n- **打通跨学科壁垒**：借助其对复数、张量网络及狄拉克符号等关键桥梁概念的详细解读，成员顺利将经典金融模型转化为量子电路设计思路。\n- **精选可靠资源池**：依托其策划的高质量列表，团队直接锁定了经过社区验证的编程语言库和软件工具，避免了在低质量资源上的无效试错。\n\nawesome-quantum-machine-learning 将原本分散晦涩的量子机器学习知识体系化，成为连接传统 AI 开发者与量子计算前沿实战的关键加速器。","https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fkrishnakumarsekar_awesome-quantum-machine-learning_0330c311.png","krishnakumarsekar","krishna kumar sekar","https:\u002F\u002Foss.gittoolsai.com\u002Favatars\u002Fkrishnakumarsekar_b031f095.jpg","E2E Developer and Artifical and Qauntum Machine Learning Research Scholar",null,"krishnakumar070891@gmail.com","https:\u002F\u002Fkrishnakumarsekar.cfapps.io\u002F","https:\u002F\u002Fgithub.com\u002Fkrishnakumarsekar",[81],{"name":82,"color":83,"percentage":84},"HTML","#e34c26",100,3337,766,"2026-04-13T08:07:30","CC0-1.0",5,"","未说明",{"notes":93,"python":91,"dependencies":94},"该项目是一个 curated list（精选列表），主要收集量子机器学习相关的算法、学习资料、库和软件资源链接，本身不是一个可直接运行的软件工具或代码库，因此没有具体的运行环境、依赖库或硬件需求。用户需根据列表中引用的具体子项目（如特定的量子计算框架或算法实现）去查阅其各自的环境要求。",[],[14,13,15],[97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115],"quantum","quantum-computing","quantum-programming-language","machine-learning","artificial-intelligence","artificial-neural-networks","tensorflow","awesome-list","awesome","machine-learning-algorithms","knn-classification","fcm","kmeans","hmm-model","qubits","ant-colony-optimization","ai","quantum-ai","qml","2026-03-27T02:49:30.150509","2026-04-14T05:00:07.365354",[],[]]