Showing 6 open source projects for "gpu"

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  • 1
    NumPy

    NumPy

    The fundamental package for scientific computing with Python

    ...NumPy offers comprehensive mathematical functions, random number generators, linear algebra routines, Fourier transforms, and more. NumPy supports a wide range of hardware and computing platforms, and plays well with distributed, GPU, and sparse array libraries. The core of NumPy is well-optimized C code. Enjoy the flexibility of Python with the speed of compiled code. NumPy’s high level syntax makes it accessible and productive for programmers from any background or experience level. Distributed under a liberal BSD license, NumPy is developed and maintained publicly on GitHub by a vibrant, responsive, and diverse community. ...
    Downloads: 75 This Week
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  • 2
    CUDA-Q

    CUDA-Q

    C++ and Python support for the CUDA Quantum programming model

    ...It provides a full toolchain that includes compilers, runtimes, and libraries for writing quantum programs in both C++ and Python. The platform is designed to be hardware-agnostic, allowing developers to run applications on different quantum backends or simulate them efficiently using GPU acceleration when physical quantum hardware is unavailable. It enables complex workflows where classical and quantum computations are tightly integrated, supporting advanced research and real-world applications in quantum computing. The repository includes components such as the nvq++ compiler and runtime systems that manage execution across heterogeneous environments.
    Downloads: 0 This Week
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  • 3
    Qulacs

    Qulacs

    Variational Quantum Circuit Simulator for Quantum Computation Research

    Variational Quantum Circuit Simulator for Quantum Computation Research. Qulacs is a Python/C++ library for fast simulation of large, noisy, or parametric quantum circuits. Qulacs is developed at QunaSys, Osaka University, NTT, and Fujitsu.
    Downloads: 0 This Week
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  • 4
    CUDA-QX

    CUDA-QX

    Accelerated libraries for quantum-classical computing built on CUDA-Q

    CUDA-QX is a collection of accelerated libraries built on top of the CUDA-Q platform, designed to enable rapid development of hybrid quantum-classical applications. It extends the CUDA-Q programming model by providing optimized implementations of domain-specific quantum computing primitives and workflows. The libraries are intended to help researchers and developers leverage GPUs, CPUs, and quantum processing units together in a unified computational model. CUDA-QX focuses on key areas such...
    Downloads: 0 This Week
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  • 5
    TorchQuantum

    TorchQuantum

    A PyTorch-based framework for Quantum Classical Simulation

    ...Researchers on quantum algorithm design, parameterized quantum circuit training, quantum optimal control, quantum machine learning, and quantum neural networks. Dynamic computation graph, automatic gradient computation, fast GPU support, batch model terrorized processing.
    Downloads: 1 This Week
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  • 6
    Paddle Quantum

    Paddle Quantum

    Paddle Quantum

    Paddle Quantum (量桨) is the world's first cloud-integrated quantum machine learning platform based on Baidu PaddlePaddle. It supports the building and training of quantum neural networks, making PaddlePaddle the first deep-learning framework in China. Paddle Quantum is feature-rich and easy to use. It provides comprehensive API documentation and tutorials help users get started right away. Paddle Quantum aims at establishing a bridge between artificial intelligence (AI) and quantum computing...
    Downloads: 0 This Week
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