Simulating non-native cubic interactions on noisy quantum machines

Yuan Shi, Alessandro R. Castelli, Xian Wu, Ilon Joseph, Vasily Geyko, Frank R. Graziani, Stephen B. Libby, Jeffrey B. Parker, Yaniv J. Rosen, Luis A. Martinez, and Jonathan L. DuBois
Phys. Rev. A 103, 062608 – Published 16 June 2021

Abstract

As a milestone for general-purpose computing machines, we demonstrate that quantum processors can be programed to efficiently simulate dynamics that are not native to the hardware. Moreover, on noisy devices without error correction, we show that simulation results are significantly improved when the quantum program is compiled using modular gates instead of a restricted set of standard gates. We demonstrate the general methodology by solving a cubic interaction problem, which appears in nonlinear optics, gauge theories, as well as plasma and fluid dynamics. To encode the non-native Hamiltonian evolution, we decompose the Hilbert space into a direct sum of invariant subspaces in which the nonlinear problem is mapped to a finite-dimensional Hamiltonian simulation problem. In a three-states example, the resultant unitary evolution is realized by a product of approximately 20 standard gates, using which approximately ten simulation steps can be carried out on state-of-the-art quantum hardware before results are corrupted by decoherence. In comparison, the simulation depth is improved by more than an order of magnitude when the unitary evolution is realized as a single cubic gate, which is compiled directly using optimal control. Alternatively, parametric gates may also be compiled by interpolating control pulses. Modular gates thus obtained provide high-fidelity building blocks for quantum Hamiltonian simulations.

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  • Received 20 June 2020
  • Revised 24 May 2021
  • Accepted 3 June 2021

DOI:https://doi.org/10.1103/PhysRevA.103.062608

©2021 American Physical Society

Physics Subject Headings (PhySH)

Nonlinear DynamicsQuantum Information, Science & TechnologyPlasma Physics

Authors & Affiliations

Yuan Shi*, Alessandro R. Castelli, Xian Wu, Ilon Joseph, Vasily Geyko, Frank R. Graziani, Stephen B. Libby, Jeffrey B. Parker, Yaniv J. Rosen, Luis A. Martinez, and Jonathan L. DuBois

  • Lawrence Livermore National Laboratory, Livermore, California 94551, USA

  • *shi9@llnl.gov

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Vol. 103, Iss. 6 — June 2021

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