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Quantum optimization experiments with advanced mixers and controls

ORAL

Abstract

Modern quantum optimization methods feature quantum circuits with partially-ordered modular levels, such as the Quantum Alternating Operator Ansatz1. For QAOA to work in practice on NISQ devices, it must extract value from quantum effects despite daunting fidelity degradation due to noise2. Our experiments on a Rigetti processor, featuring these kind of algorithms, employ calibrated parametric CZ3 and XY4,5 gates, as well as analog-controlled phases between qubit pairs programmed with a low-level pulse design language (Quilt). We show experimental benchmark results on a 32-qubit chip for circuits related to hard-constrained scheduling problems as well as MaxCut.

1Hadfield et al. 2019. From the quantum approximate optimization algorithm to a quantum alternating operator ansatz. Algorithms 12(2)
2Marshall et al. 2020. Characterizing local noise in QAOA circuits. arXiv:2002.11682
3Caldwell et al. 2018. Parametrically activated entangling gates using transmon qubits. PRApplied 10(3):034050
4Abrams et al. 2019. Implementation of the XY interaction family with calibration of a single pulse. arXiv:1912.04424
5Wang et al. 2020. XY mixers: Analytical and numerical results for the quantum alternating operator ansatz. PRA 101(1), 012320

Presenters

  • Davide Venturelli

    NASA Ames Research Center

Authors

  • M. Sohaib Alam

    Rigetti Computing

  • Shon Grabbe

    NASA Ames Research Center

  • Alexander Hill

    Rigetti Quantum Computing, Rigetti Computing

  • Mark Hodson

    Rigetti Computing

  • Zoe Gonzalez Izquierdo

    NASA Ames Research Center

  • Ryan LaRose

    NASA Ames Research Center, Michigan State University, Unitary Fund

  • Aaron Lott

    NASA Ames Research Center

  • Matt Reagor

    Rigetti Computing

  • Eleanor G Rieffel

    NASA Ames Research Center, Quantum AI Lab, NASA Ames Research Center

  • James Sud

    NASA Ames Research Center

  • Davide Venturelli

    NASA Ames Research Center

  • Zhihui Wang

    NASA Ames Research Center, USRA Research Institute for Advanced Computer Science (RIACS), Mountain View, CA 94043, USA, Quantum AI Lab, NASA Ames Research Center; USRA

  • Filip Wudarski

    NASA Ames Research Center