Open-source alternatives to Qiskit

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

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

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

This repository serves as a comprehensive research platform and toolkit for advancing machine learning, quantum computing, and large-scale scientific data analysis. It provides foundational frameworks for developing complex algorithmic systems, offering the necessary infrastructure for distributed training, computational graph execution, and high-performance model development. The project distinguishes itself by integrating specialized research domains with robust, privacy-preserving methodologies. It supports diverse scientific discovery through tools for quantum simulation, physics-informed

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

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

ruvector is a Rust-based vector store and graph database designed for local inference and nearest neighbor searches. It utilizes a vector graph database architecture and a graph neural network index to refine search rankings through structural attention. The system includes a hardware-accelerated quantum circuit simulator for executing state-vector simulations and complex search patterns, alongside a WebAssembly inference engine for running vector search and model execution directly in web browsers. The project employs a cognitive container format that bundles models, data, and a bootable mic

QML: Quantum Machine Learning

This project is a Chinese language translation of the technical guides and API references for the PyTorch deep learning framework. It serves as a localized knowledge base and reference material to make deep learning documentation accessible to non-English speakers. The documentation covers a comprehensive range of PyTorch capabilities, including neural network model development, automatic differentiation, and the implementation of backend kernels. It provides detailed guidance on distributed training strategies, model deployment through formats like ONNX and C++, and various model optimizatio

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

SeaTunnel is a distributed data integration engine designed to synchronize structured and unstructured data across diverse sources and sinks. It functions as a multi-engine execution framework that can run data integration tasks across different distributed computing backends to optimize workload performance. The project is distinguished by a visual data pipeline designer for configuring workflows without manual code and a specialized change data capture tool for streaming incremental database updates. It also includes an enrichment pipeline that integrates large language models and embedding

This project is a cryptographic mining protocol that establishes a distributed compute network for solving complex mathematical tasks. It functions as a decentralized infrastructure where registered mining nodes participate in proof of work mining to solve network problems in exchange for rewards. The system specializes in quantum-inspired problem solving by mapping tasks into Ising mathematical structures. These problems are processed using a hardware-agnostic computation model, allowing solvers to execute tasks across CPU, GPU, or quantum processing units. The protocol includes tools for m

Tengine is a suite of tooling and a lightweight execution engine designed for running deep learning models on constrained embedded hardware. It provides an infrastructure for converting neural network models, quantizing weights, optimizing operator kernels, and benchmarking inference performance across CPU, GPU, and NPU units. The project features an automated operator kernel optimizer to generate high-efficiency kernels and a model quantization tool that reduces precision to integer formats to lower memory usage. It includes a dedicated hardware benchmarking tool to evaluate the execution sp

GHDL is a compiler and simulator for VHDL hardware descriptions. It functions as a multi-pass analysis elaborator that resolves design hierarchies and dependencies to prepare hardware descriptions for simulation or synthesis. The project transforms VHDL source code into executable binaries for high-speed digital design verification and serves as a synthesis tool that converts descriptions into structural netlists compatible with vendor or open-source flows. It also implements the Language Server Protocol to provide static analysis, autocomplete, and code navigation for VHDL files. The toolse

Nextflow is a dataflow workflow engine and distributed computing framework used to build and execute data-intensive pipelines. It serves as a scientific workflow language that allows users to define reproducible data processing sequences, supporting any scripting language through shebang declarations. The system functions as a containerized pipeline orchestrator, utilizing container technologies to ensure software dependencies remain consistent across different environments. It decouples workflow logic from the underlying infrastructure, enabling the same pipeline to run on local machines, cl

Latex-template-collection is a collection of pre-formatted LaTeX and XeTeX templates for generating academic papers, resumes, invoices, letters, and presentations. It provides reusable document skeletons and custom styling commands that separate layout geometry from raw textual content, supported by multi-pass PDF compilation engines and external bibliography integration. The templates span academic research publishing, business billing, formal correspondence, resume formatting, and slide presentation design. Included layouts cover research papers, thesis proposals, curriculum vitae, poetry,

This project is a compiler development tutorial that provides a series of guides and exercises for building a complete compiler from scratch. It focuses on the implementation of a structured compilation pipeline to transform high-level source code into executable machine instructions. The project covers the creation of a machine code generator for specific processor architectures and a static analysis framework. This framework includes methodologies for implementing type checking and constant folding to verify logic correctness before the final execution phase. The instructional material enc

Civitai is a platform for generative media creation and AI model distribution. It provides a centralized service for producing images, videos, audio, and music, while serving as a repository where users can share, discover, and browse custom model weights and fine-tuned adaptations. The platform distinguishes itself through a provider-agnostic orchestration layer that manages multi-step generation pipelines and complex workflows across different backends. It integrates with autonomous AI agents and editors via the Model Context Protocol, allowing external tools to access generation pipelines

Neural Compressor is a deep learning model compression toolkit and AI inference acceleration engine. It functions as an automated model quantization tool and hardware-aware model compiler designed to reduce the memory footprint of neural networks and decrease execution latency. The project provides specialized frameworks for optimizing large language models, utilizing weight-only quantization and hardware-specific kernels to improve the operational efficiency of generative AI workloads. It maps neural network operators to specialized CPU and GPU vector instructions to accelerate model executi

This project is a Rust interface for the PyTorch C++ library, serving as a deep learning framework and tensor computing library. It functions as a C++ API wrapper that enables the manipulation of multi-dimensional arrays and the execution of neural network architectures across CPU and GPU hardware accelerators. The library provides a TorchScript inference engine to load and execute just-in-time compiled models. It also supports Rust and Python interoperability, allowing for the creation of Python extensions that share tensor data through a common interface. The system covers deep learning mo

ILSpy is a .NET decompiler and binary analyzer designed to convert compiled .NET assemblies back into readable C# source code. It functions as a metadata explorer and a common intermediate language viewer, enabling the analysis of compiled code and the execution of reverse engineering workflows. The project distinguishes itself through specialized translation capabilities, such as converting compiled binary XML (BAML) back into human-readable XAML for user interface analysis. It also provides tools for inspecting native machine code and extracting metadata from program database (PDB) files.

This project is a technical guide to the Go language internals, focusing on the analysis of the Go compiler, runtime, and toolchain. It serves as a resource for studying the official source code to understand the technical principles of how the system operates. The resource provides a deconstruction of the Go build process, tracing code from lexing and parsing through optimization passes and intermediate representations to the final machine binary. It includes analysis of the Go scheduler and garbage collector to examine memory management and execution patterns. The project covers the visual

This project is a JavaScript optimizer, minifier, module bundler, transpiler, and static analysis tool. It provides a compilation pipeline designed to shrink file sizes and improve runtime performance. The system utilizes a multi-pass compilation process to perform dead code elimination, global name mangling, and static type inference. It identifies unreachable functions and unused variables to reduce the final output size and detects potential runtime errors without executing the code. The tool manages assets through dependency resolution, code chunking, and bundle management. It ensures co

Accelerate is a framework for high-performance array computing that provides a domain-specific language for expressing complex mathematical and parallel computations. By utilizing a declarative programming interface, it allows users to define high-level array transformations that are automatically translated into optimized machine code for diverse hardware architectures. The system distinguishes itself through a modular architecture that decouples high-level array operations from hardware-specific instructions. It employs just-in-time compilation and kernel fusion to transform programs into e

This project is a multi-language compiler collection and cross-platform toolchain used to translate source code from various programming languages into optimized machine code for different hardware architectures. It provides a suite of tools including an optimizing compiler backend, a machine code generator, and a comprehensive runtime library suite that implements necessary execution environments and support functions. The system utilizes a multi-pass compilation pipeline and pluggable language front-ends to process source code into intermediate representations. It distinguishes itself throu

This project is a high-performance compiler written in Rust that transpiles TypeScript and ECMAScript into compatible JavaScript code. It functions as a TypeScript transpiler, a JavaScript minifier, and a JavaScript bundler. The system distinguishes itself through a WebAssembly plugin host that allows the execution of custom transformation rules without modifying the core binary. It also provides specialized compilation for React source code to improve runtime performance and reduce execution overhead. The broader capability surface includes source-to-source compilation, type annotation stri