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Perturbative Quantum Simulation

Jinzhao Sun, Suguru Endo, Huiping Lin, Patrick Hayden, Vlatko Vedral, and Xiao Yuan
Phys. Rev. Lett. 129, 120505 – Published 15 September 2022
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Abstract

Approximation based on perturbation theory is the foundation for most of the quantitative predictions of quantum mechanics, whether in quantum many-body physics, chemistry, quantum field theory, or other domains. Quantum computing provides an alternative to the perturbation paradigm, yet state-of-the-art quantum processors with tens of noisy qubits are of limited practical utility. Here, we introduce perturbative quantum simulation, which combines the complementary strengths of the two approaches, enabling the solution of large practical quantum problems using limited noisy intermediate-scale quantum hardware. The use of a quantum processor eliminates the need to identify a solvable unperturbed Hamiltonian, while the introduction of perturbative coupling permits the quantum processor to simulate systems larger than the available number of physical qubits. We present an explicit perturbative expansion that mimics the Dyson series expansion and involves only local unitary operations, and show its optimality over other expansions under certain conditions. We numerically benchmark the method for interacting bosons, fermions, and quantum spins in different topologies, and study different physical phenomena, such as information propagation, charge-spin separation, and magnetism, on systems of up to 48 qubits only using an 8+1 qubit quantum hardware. We demonstrate our scheme on the IBM quantum cloud, verifying its noise robustness and illustrating its potential for benchmarking large quantum processors with smaller ones.

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  • Received 17 November 2021
  • Revised 27 January 2022
  • Accepted 17 August 2022

DOI:https://doi.org/10.1103/PhysRevLett.129.120505

© 2022 American Physical Society

Physics Subject Headings (PhySH)

Quantum Information, Science & Technology

Authors & Affiliations

Jinzhao Sun1,2,3, Suguru Endo4, Huiping Lin1,5, Patrick Hayden6, Vlatko Vedral2,7, and Xiao Yuan1,5,6,*

  • 1Center on Frontiers of Computing Studies, Peking University, Beijing 100871, China
  • 2Clarendon Laboratory, University of Oxford, Parks Road, Oxford OX1 3PU, United Kingdom
  • 3Quantum Advantage Research, Beijing 100080, China
  • 4NTT Computer & Data Science Laboratories, NTT corporation, Musashino, Tokyo 180-8585, Japan
  • 5School of Computer Science, Peking University, Beijing 100871, China
  • 6Stanford Institute for Theoretical Physics, Stanford University, Stanford, California 94305, USA
  • 7Centre for Quantum Technologies, National University of Singapore, Singapore 117543, Singapore

  • *xiaoyuan@pku.edu.cn

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Issue

Vol. 129, Iss. 12 — 16 September 2022

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