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Quantum computing frameworks

Ranking updated Jul 24, 2026

For quantum computing frameworks, the strongest matches are qiskit/qiskit (Qiskit is a comprehensive quantum computing framework providing circuit), quantumlib/cirq (Cirq is a Python-based quantum computing framework that supports) and microsoft/quantum (Microsoft Quantum is a quantum computing framework offering hybrid). quipnetwork/xq-py and originq/qpanda-2 round out the shortlist. Each is ranked by relevance to your query, popularity and recent activity.

Compare the top open-source quantum computing frameworks, ranked by stars and activity, and find the right one for your project.

Quantum computing frameworks

Find the best repos with AI.We'll search the best matching repositories with AI.
  • qiskit/qiskitQiskit avatar

    Qiskit/qiskit

    7,522View on GitHub↗

    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

    Qiskit is a comprehensive quantum computing framework providing circuit design, simulation, hardware-agnostic execution, and noise mitigation tools wrapped in a Python SDK.

    PythonHybrid Quantum-Classical ComputingQuantum SimulatorsHybrid Quantum-Classical Execution
    View on GitHub↗7,522
  • quantumlib/cirqquantumlib avatar

    quantumlib/Cirq

    4,990View on GitHub↗

    Cirq is a Python quantum computing framework used for designing, simulating, and executing quantum circuits on Noisy Intermediate-Scale Quantum (NISQ) hardware. It serves as a quantum circuit simulator and noise modeler, as well as a tool for the implementation of quantum algorithms. The framework provides a specialized interface for NISQ hardware, allowing users to map logical quantum circuits to physical device topologies while validating hardware connectivity and gate constraints. It distinguishes itself through integrated noise modeling, applying depolarizing and damping channels to mimic

    Cirq is a Python-based quantum computing framework that supports circuit design, simulation, noise modeling, and hardware-agnostic execution for quantum algorithms.

    PythonNoise ModelingQuantum SimulatorsQuantum Algorithm Implementation
    View on GitHub↗4,990
  • microsoft/quantummicrosoft avatar

    microsoft/Quantum

    4,043View on GitHub↗

    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

    Microsoft Quantum is a quantum computing framework offering hybrid quantum-classical workflows, hardware simulation, and a built-in algorithm library, though its heavy reliance on the Q# language differs from a pure Python-only interface.

    Jupyter NotebookHybrid Quantum-Classical ComputingQuantum SimulatorsHybrid Quantum-Classical Execution
    View on GitHub↗4,043
  • quipnetwork/xq-pyQuipNetwork avatar

    QuipNetwork/xq-py

    5,546View on GitHub↗

    xq-py is a numerical quantum computing library and software emulator used to execute quantum algorithms. It functions as a quantum virtual machine that simulates quantum circuits and state vectors through the use of linear algebra and complex number arrays. The project provides a virtual environment for developing and verifying quantum logic. It models multi-qubit systems by utilizing tensor-product expansion and unitary gate applications to simulate quantum state vectors and calculate probabilistic state collapse. The simulation is supported by a numerical backend that handles the matrix-ba

    It is a quantum computing library that simulates quantum circuits and state vectors, though it lacks explicit mentions of noise modelling or a broader algorithm library.

    Quantum SimulatorsQuantum ComputingQuantum Circuit Design
    View on GitHub↗5,546
  • originq/qpanda-2OriginQ avatar

    OriginQ/QPanda-2

    1,203View on GitHub↗

    QPanda 2 is an open source quantum computing framework developed by OriginQC that can be used to build, run, and optimize quantum algorithms.

    QPanda 2 is a C++ quantum computing framework with a Python interface that provides tools to build, run, and optimize quantum algorithms, though it is narrower on certain advanced noise-modelling features compared to the most comprehensive platforms.

    C++Quantum Algorithms
    View on GitHub↗1,203
  • paddlepaddle/quantumPaddlePaddle avatar

    PaddlePaddle/Quantum

    646View on GitHub↗

    English | 简体中文

    This repository provides a development framework with quantum algorithms and a Python interface for designing and simulating quantum circuits, fitting the category well despite a missing tagline.

    Jupyter NotebookQuantum Algorithms
    View on GitHub↗646
  • quantumbfs/yao.jlQuantumBFS avatar

    QuantumBFS/Yao.jl

    1,033View on GitHub↗

    Extensible, Efficient Quantum Algorithm Design for Humans.

    Yao.jl is a quantum computing framework built for extensible algorithm design and simulation in Julia, though it lacks the Python interface typically expected for these workflows.

    JuliaQuantum Simulators
    View on GitHub↗1,033
  • pennylaneai/pennylanePennyLaneAI avatar

    PennyLaneAI/pennylane

    3,267View on GitHub↗

    PennyLane is an open-source quantum software platform for quantum computing, quantum machine learning, and quantum chemistry. Create meaningful quantum algorithms, from inspiration to implementation.

    PennyLane is a comprehensive quantum software platform that provides a Python interface for quantum circuit simulation, hybrid quantum-classical computing, and differentiable quantum algorithms, though it lacks direct focus on hardware-agnostic execution and noise mitigation out-of-the-box.

    PythonQuantum Computing
    View on GitHub↗3,267
  • qutip/qutipqutip avatar

    qutip/qutip

    2,032View on GitHub↗

    QuTiP: Quantum Toolbox in Python

    QuTiP is a Python-based quantum toolbox designed for simulating the dynamics of open quantum systems, though it focuses more on physical systems and quantum optics than standard quantum circuit execution.

    PythonQuantum Computing
    View on GitHub↗2,032
  • projectq-framework/projectqProjectQ-Framework avatar

    ProjectQ-Framework/ProjectQ

    975View on GitHub↗

    ProjectQ: An open source software framework for quantum computing

    ProjectQ is an open-source Python framework for quantum computing that lets you design and simulate quantum circuits, making it a good fit for this search even though it lacks some advanced noise mitigation features.

    PythonQuantum Programming Frameworks
    View on GitHub↗975
  • xanaduai/strawberryfieldsX

    xanaduai/strawberryfields

    0View on GitHub↗

    Strawberry Fields is a Python-based quantum computing framework designed for simulating and executing quantum circuits, focusing specifically on continuous-variable quantum information and optical quantum computing.

    Quantum Programming Frameworks
    View on GitHub↗0
  • mit-han-lab/torchquantummit-han-lab avatar

    mit-han-lab/torchquantum

    1,632View on GitHub↗

    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

    This PyTorch-based framework provides quantum circuit simulation and quantum machine learning integration, serving as a specialized tool for quantum-classical computing even though it lacks some broader hardware-agnostic execution features.

    Jupyter NotebookQuantum SimulatorsQuantum ComputingQuantum-Classical Hybrid Models
    View on GitHub↗1,632
Compare the top 10 at a glance
RepositoryStarsLanguageLicenseLast push
qiskit/qiskit7.5KPythonApache-2.0Jun 23, 2026
quantumlib/cirq5KPythonApache-2.0Jun 16, 2026
microsoft/quantum4KJupyter NotebookMITJan 12, 2024
quipnetwork/xq-py5.5K——Jun 3, 2026
originq/qpanda-21.2KC++Apache-2.0Nov 13, 2024
paddlepaddle/quantum646Jupyter NotebookNOASSERTIONApr 24, 2023
quantumbfs/yao.jl1KJuliaNOASSERTIONMay 23, 2026
pennylaneai/pennylane3.3KPythonApache-2.0Jun 16, 2026
qutip/qutip2KPythonBSD-3-ClauseMay 27, 2026
projectq-framework/projectq975PythonApache-2.0May 25, 2026

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