论文标题
TensorCircuit：NISQ时代的量子软件框架
TensorCircuit: a Quantum Software Framework for the NISQ Era
论文作者
论文摘要
TensorCircuit是一种基于张量网络收缩的开源量子电路模拟器，旨在速度，灵活性和代码效率。 TensorCircuit纯粹是用Python编写的，并建在行业标准的机器学习框架之上，支持自动差异化，即时汇编，矢量化的并行性和硬件加速度。这些功能使张力循环能够模拟比现有模拟器更大，更复杂的量子电路，并且特别适合基于参数化量子电路的变异算法。与其他通用量子软件相比，TensorCircuit可以为各种量子模拟任务提供数量级加速，并且可以模拟具有中等电路深度和低维连接性的多达600 QUIT。凭借其时间和空间效率，灵活，可扩展的体系结构以及紧凑，用户友好的API，已构建了TensorCircuit，以促进嘈杂的中等规模量子（NISQ）时代的量子算法的设计，模拟和分析。
TensorCircuit is an open source quantum circuit simulator based on tensor network contraction, designed for speed, flexibility and code efficiency. Written purely in Python, and built on top of industry-standard machine learning frameworks, TensorCircuit supports automatic differentiation, just-in-time compilation, vectorized parallelism and hardware acceleration. These features allow TensorCircuit to simulate larger and more complex quantum circuits than existing simulators, and are especially suited to variational algorithms based on parameterized quantum circuits. TensorCircuit enables orders of magnitude speedup for various quantum simulation tasks compared to other common quantum software, and can simulate up to 600 qubits with moderate circuit depth and low-dimensional connectivity. With its time and space efficiency, flexible and extensible architecture and compact, user-friendly API, TensorCircuit has been built to facilitate the design, simulation and analysis of quantum algorithms in the Noisy Intermediate-Scale Quantum (NISQ) era.
