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Verifying Random Quantum Circuits with Arbitrary Geometry Using Tensor Network States Algorithm.
Guo, Chu; Zhao, Youwei; Huang, He-Liang.
Affiliation
  • Guo C; 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.
  • Zhao Y; Hefei National Laboratory for Physical Sciences at the Microscale and Department of Modern Physics, University of Science and Technology of China, Hefei 230026, China.
  • Huang HL; Shanghai Branch, CAS Center for Excellence in Quantum Information and Quantum Physics, University of Science and Technology of China, Shanghai 201315, China.
Phys Rev Lett ; 126(7): 070502, 2021 Feb 19.
Article de En | MEDLINE | ID: mdl-33666457
ABSTRACT
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.

Texte intégral: 1 Collection: 01-internacional Base de données: MEDLINE Type d'étude: Clinical_trials / Prognostic_studies Langue: En Journal: Phys Rev Lett Année: 2021 Type de document: Article Pays d'affiliation: Chine

Texte intégral: 1 Collection: 01-internacional Base de données: MEDLINE Type d'étude: Clinical_trials / Prognostic_studies Langue: En Journal: Phys Rev Lett Année: 2021 Type de document: Article Pays d'affiliation: Chine