• Open Access

Optimized SWAP networks with equivalent circuit averaging for QAOA

Akel Hashim, Rich Rines, Victory Omole, Ravi K. Naik, John Mark Kreikebaum, David I. Santiago, Frederic T. Chong, Irfan Siddiqi, and Pranav Gokhale
Phys. Rev. Research 4, 033028 – Published 11 July 2022

Abstract

The SWAP network is a qubit routing sequence that can be used to efficiently execute the Quantum Approximate Optimization Algorithm (QAOA). Even with a minimally connected topology on an n-qubit processor, this routing sequence enables O(n2) operations to execute in O(n) steps. In this work, we optimize the execution of SWAP networks for QAOA through two techniques. First, we take advantage of an overcomplete set of native hardware operations [including 150-ns controlled-π2 phase gates with up to 99.67(1)% fidelity] to decompose the relevant quantum gates and SWAP networks in a manner which minimizes circuit depth and maximizes gate cancellation. Second, we introduce equivalent circuit averaging, which randomizes over degrees of freedom in the quantum circuit compilation to reduce the impact of systematic coherent errors. Our techniques are experimentally validated at the Advanced Quantum Testbed through the execution of QAOA circuits for finding the ground state of two- and four-node Sherrington-Kirkpatrick spin-glass models with various randomly sampled parameters. We observe a 60% average reduction in error (total variation distance) for QAOA of depth p=1 on four transmon qubits on a superconducting quantum processor.

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  • Received 11 November 2021
  • Accepted 18 May 2022

DOI:https://doi.org/10.1103/PhysRevResearch.4.033028

Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

Published by the American Physical Society

Physics Subject Headings (PhySH)

Quantum Information, Science & Technology

Authors & Affiliations

Akel Hashim1,2,3,*, Rich Rines4,*, Victory Omole4, Ravi K. Naik1,3, John Mark Kreikebaum1,5,†, David I. Santiago3, Frederic T. Chong4,6, Irfan Siddiqi1,3,5, and Pranav Gokhale4,‡

  • 1Quantum Nanoelectronics Laboratory, Department of Physics, University of California at Berkeley, Berkeley, California 94720, USA
  • 2Graduate Group in Applied Science and Technology, University of California at Berkeley, Berkeley, California 94720, USA
  • 3Computational Research Division, Lawrence Berkeley National Lab, Berkeley, California 94720, USA
  • 4Super.tech, a division of ColdQuanta, Chicago, Illinois 60615, USA
  • 5Materials Sciences Division, Lawrence Berkeley National Lab, Berkeley, California 94720, USA
  • 6University of Chicago, Chicago, Illinois 60637, USA

  • *These authors contributed equally to this work.
  • Now at Google Quantum AI, Mountain View, CA, USA.
  • pranav.gokhale@coldquanta.com

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Vol. 4, Iss. 3 — July - September 2022

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