Showing 3 open source projects for "automatic differentiation"

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  • 1
    Yao

    Yao

    Extensible, Efficient Quantum Algorithm Design for Humans

    An intermediate representation to construct and manipulate your quantum circuit and let you make own abstractions on the quantum circuit in native Julia. Yao supports both forward-mode (faithful gradient) and reverse-mode automatic differentiation with its builtin engine optimized specifically for quantum circuits. Top performance for quantum circuit simulations. Its CUDA backend and batched quantum register support can make typical quantum circuits even faster. Yao is designed to be extensible. Its hierarchical architecture allows you to extend the framework to support and share your new algorithm and hardware.
    Downloads: 1 This Week
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  • 2
    Tequila

    Tequila

    A High-Level Abstraction Framework for Quantum Algorithms

    Tequila is an abstraction framework for (variational) quantum algorithms. It operates on abstract data structures allowing the formulation, combination, automatic differentiation and optimization of generalized objectives. Tequila can execute the underlying quantum expectation values on state-of-the-art simulators as well as on real quantum devices.
    Downloads: 1 This Week
    Last Update:
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  • 3
    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
    Last Update:
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