Awesome GitHub RepositoriesQuantum Circuit Design

这是一个全面的、由社区策划的目录，组织了庞大的 Python 软件库、框架和工具生态。它作为一个中心化知识库，旨在促进生态导航并加速开发者在整个软件开发生命周期中的发现过程。 该目录通过提供按技术领域分类的结构化资源索引脱颖而出，范围从基础开发工具到专业工程领域。它涵盖了人工智能、数据科学、Web 开发和基础设施管理等高级能力，使开发者能够为特定的技术挑战识别经过验证的解决方案。 该项目涵盖了广泛的能力领域，包括依赖管理、静态代码分析和自动化测试工具。它还编目了用于持久数据存储、云基础设施编排和接口开发的资源，为构建和维护复杂软件系统提供了统一的参考。

The AWS Cloud Development Kit is an infrastructure-as-code framework that enables developers to define and provision cloud resources using familiar programming languages. By utilizing construct-based synthesis, it translates high-level, object-oriented code into declarative templates, allowing for the automated management of complex cloud environments through a centralized, code-driven control plane. The framework distinguishes itself through its ability to model infrastructure as a dependency-aware resource graph, ensuring that components are provisioned and updated in the correct order. It

Boto3 is the AWS SDK for Python, providing a programmatic interface for managing and automating AWS cloud infrastructure and services. It serves as a cloud management API client and resource manager for provisioning, configuring, and scaling virtual servers, databases, and storage. The library enables the implementation of infrastructure-as-code through declarative templates and scripts, allowing for the deployment of identical resource stacks across multiple accounts and geographic regions. It also provides a framework for coordinating distributed workflows, serverless functions, and contain

jetson-inference is a set of libraries and tools for executing optimized deep learning models on embedded GPU hardware. Its primary purpose is to enable real-time computer vision and AI inference at the edge with low latency and high throughput. The project distinguishes itself through high-performance streaming analytics and the ability to execute concurrent AI pipelines on auto-grade silicon. It provides specialized support for multi-sensor stream processing, utilizing zero-copy data transport to load camera frames directly into GPU memory. The codebase covers a broad surface of capabiliti

Qiskit is a quantum computing software development kit used for designing, simulating, and executing quantum circuits on physical hardware and simulators. It functions as a quantum algorithm framework, a circuit simulator, and a vendor-agnostic hardware interface for dispatching workloads across diverse providers. The project features a quantum circuit transpiler that optimizes abstract designs to match the specific basis gates and qubit connectivity of target hardware. It employs a pass-based transpilation pipeline and symbolic instruction translation to convert high-level circuits into hard

A bytecode optimizer for Android apps

xq-py 是一个数值量子计算库和软件模拟器，用于执行量子算法。它作为一个量子虚拟机，通过使用线性代数和复数数组来模拟量子电路和状态向量。 该项目提供了一个用于开发和验证量子逻辑的虚拟环境。它通过利用张量积展开和幺正门应用来模拟多量子比特系统，从而模拟量子状态向量并计算概率性状态坍缩。 该模拟由一个数值后端支持，该后端处理量子计算仿真所需的基于矩阵的状态转换和浮点数学运算。

Cirq 是一个 Python 量子计算框架，用于在含噪声中等规模量子（NISQ）硬件上设计、模拟和执行量子电路。它既是一个量子电路模拟器和噪声建模器，也是实现量子算法的工具。 该框架为 NISQ 硬件提供了专门的接口，允许用户将逻辑量子电路映射到物理设备拓扑，同时验证硬件连接性和门约束。它通过集成噪声建模脱颖而出，应用去极化和阻尼通道来模拟真实量子处理器中发现的退相干和错误。 该项目涵盖了广泛的功能，包括量子电路设计、硬件集成和状态模拟。它包括用于门分解、硬件拓扑映射以及执行傅里叶变换和非结构化数据搜索等基本量子过程的工具。此外，它还提供了用于分子基态计算和硬件保真度基准测试的分析工具。

Chisel is a hardware construction language and description tool used to define digital circuits. It functions as a generator that converts high-level hardware descriptions into synthesizable Verilog code for use in ASIC and FPGA design. The project enables the creation of parameterizable hardware templates and reusable digital components. It leverages functional and object-oriented programming patterns to transform complex circuit representations into finalized hardware descriptions. The toolset covers the register-transfer level design workflow, allowing users to model digital circuits usin

Yosys 是一个数字逻辑综合器和 RTL 综合工具，可将硬件描述语言代码转换为优化的门级表示。它作为硬件逻辑优化器和形式验证工具，提供了一个将通用电路表示转换为特定技术库单元的框架，以适配目标硬件平台。 该项目通过可脚本化的环境充当硬件设计自动化工具，用于执行综合命令序列。这种自动化允许管理复杂的数字硬件工作流，将 RTL 设计综合与技术单元映射集成在一起。 其验证和分析能力包括符号模型检查和设计等价性验证。这些工具通过检查安全性和活性属性并生成仿真测试平台，支持形式化硬件验证，确保设计符合其原始规范。

ruvector 是一个基于 Rust 的向量存储和图数据库，专为本地推理和最近邻搜索而设计。它利用向量图数据库架构和图神经网络索引，通过结构化注意力机制优化搜索排名。该系统包括用于执行状态向量模拟和复杂搜索模式的硬件加速量子电路模拟器，以及用于直接在 Web 浏览器中运行向量搜索和模型执行的 WebAssembly 推理引擎。 该项目采用认知容器格式，将模型、数据和可启动微内核打包成单个二进制文件进行部署。它具有专门的模型配置工具，包括防止灾难性遗忘的权重合并方法，以及用于即时权重适配的轻量级适配器机制。 该系统涵盖了广泛的功能面，包括硬件加速向量搜索、图关系查询以及用于 LaTeX 和 MathML 提取的科学文档解析。它还提供用于验证数据变更的加密见证链、基于 Raft 的高可用元数据同步，以及用于管理存储成本的分层分辨率数据压缩。

Quantum is a quantum development framework that provides a hybrid quantum-classical workflow for coordinating execution between classical host languages and quantum processors. It includes a quantum hardware simulator for modeling state evolution and a library of quantum algorithms for tasks such as integer factorization, database search, and quantum arithmetic. The project offers specialized tools for quantum hardware characterization and error correction to manage noise and decoherence. It provides resource estimation modeling to calculate the qubit count and gate depth required to execute

这是一个使用 TensorFlow 2 构建、训练和部署机器学习模型的综合教育资源和教程手册。它作为结构化学习指南，涵盖了深度学习的核心概念，包括神经网络架构、自动微分和张量运算。 该手册提供了关于通过 GPU 内存管理、分布式训练和模型量化来优化执行效率的技术指导。它还包括用于构建高性能数据管道以及将模型导出到生产服务器、移动设备和 Web 浏览器的详细手册。 该材料涵盖了广泛的功能，包括使用卷积和循环网络的模型开发、自定义损失函数和层的实现，以及使用预训练模型进行迁移学习。它还探讨了边缘设备的部署策略以及使用基于云的运行时进行硬件加速。 该资源以 Jupyter Notebooks 集合的形式实现。

atopile is an electronic design automation software and hardware description language that enables the definition of electronic circuits and components as code. It functions as a modular electronics framework and a compiler that transforms programmatic circuit definitions into a normalized model and KiCad project files for physical layout. The system distinguishes itself through automated hardware design automation, using a constraint-based value solver to calculate required component values and validate electrical specifications during the build process. It employs a modular approach to hard

This project is a quantum computing educational resource and implementation library. It provides a collection of interactive notebooks and guides designed for learning quantum programming, developing algorithms, and simulating quantum circuits. The resource includes tutorials for implementing standard quantum algorithms and creating custom circuit passes. It specifically covers quantum hardware control, providing instructions on scheduling raw microwave or laser pulses to implement precise gates at the physical layer. The materials cover the broader surface of quantum circuit design, includi

Torchquantum is a tensor-based quantum machine learning library and simulation engine that integrates parameterized quantum circuits directly into PyTorch training pipelines. It enables the construction of hybrid quantum-classical neural networks where quantum operations function as differentiable layers within standard deep learning architectures. The framework computes analytical parameter gradients using native automatic differentiation engines and parameter-shift rules, facilitating end-to-end training and backpropagation. It supports tensor-based quantum state vector simulations, specia