Guo Chu, Zhao Youwei, Huang He-Liang
Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education, Department of Physics and Synergetic Innovation Center for Quantum Effects and Applications, Hunan Normal University, Changsha 410081, China.
Hefei National Laboratory for Physical Sciences at the Microscale and Department of Modern Physics, University of Science and Technology of China, Hefei 230026, China.
Phys Rev Lett. 2021 Feb 19;126(7):070502. doi: 10.1103/PhysRevLett.126.070502.
The ability to efficiently simulate random quantum circuits using a classical computer is increasingly important for developing noisy intermediate-scale quantum devices. Here, we present a tensor network states based algorithm specifically designed to compute amplitudes for random quantum circuits with arbitrary geometry. Singular value decomposition based compression together with a two-sided circuit evolution algorithm are used to further compress the resulting tensor network. To further accelerate the simulation, we also propose a heuristic algorithm to compute the optimal tensor contraction path. We demonstrate that our algorithm is up to 2 orders of magnitudes faster than the Schrödinger-Feynman algorithm for verifying random quantum circuits on the 53-qubit Sycamore processor, with circuit depths below 12. We also simulate larger random quantum circuits with up to 104 qubits, showing that this algorithm is an ideal tool to verify relatively shallow quantum circuits on near-term quantum computers.
利用经典计算机高效模拟随机量子电路的能力对于开发有噪声的中尺度量子设备越来越重要。在此,我们提出一种基于张量网络态的算法,该算法专门设计用于计算具有任意几何结构的随机量子电路的振幅。基于奇异值分解的压缩与双边电路演化算法一起用于进一步压缩所得的张量网络。为了进一步加速模拟,我们还提出一种启发式算法来计算最优张量收缩路径。我们证明,对于在53比特的Sycamore处理器上验证深度小于12的随机量子电路,我们的算法比薛定谔 - 费曼算法快达2个数量级。我们还模拟了多达104比特的更大随机量子电路,表明该算法是在近期量子计算机上验证相对较浅量子电路的理想工具。