Quantum approximate optimization on a gate-model superconducting processor

ORAL

Abstract

The Quantum Approximate Optimization Algorithm (QAOA) [Farhi et al. arXiv:1411.4028] is a promising application for near-term quantum computing devices and has potential to demonstrate quantum supremacy [Farhi & Harrow arXiv:1602.07674]. We compile and run the QAOA algorithm on a programmable superconducting qubit processor, assessing its performance on instances of the MAX-CUT and k-coloring problems. Experimental performance is analyzed to measure the robustness of QAOA to device noise, and we comment on its outlook as a near-term demonstration of quantum supremacy.

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Authors

  • William Zeng

    • Rigetti Quantum Computing

  • Nicholas Rubin

    • Rigetti Quantum Computing

  • Michael Curtis

    • Rigetti Quantum Computing

  • Anthony Polloreno

    • Rigetti Quantum Computing

  • Robert Smith

    • Rigetti Quantum Computing

  • Joel Angeles

    • Rigetti Quantum Computing

  • Benjamin Bloom

    • Rigetti Quantum Computing

  • Maxwell Block

    • Rigetti Quantum Computing

  • Shane Caldwell

    • Rigetti Quantum Computing

  • William O'Brien

    • Rigetti Quantum Computing

  • Alexander Papageorge

    • Rigetti Quantum Computing

  • Russ Renzas

    • Rigetti Quantum Computing

  • Damon Russell

    • Rigetti Quantum Computing

  • Diego Scarabelli

    • Rigetti Quantum Computing

  • Michael Scheer

    • Rigetti Quantum Computing

  • Eyob Sete

    • Rigetti Quantum Computing

  • Rodney Sinclair

    • Rigetti Quantum Computing

  • Nikolas Tezak

    • Rigetti Quantum Computing

  • Mehrnoosh Vahidpour

    • Rigetti Quantum Computing

  • Marius Villiers

    • Rigetti Quantum Computing

  • Alexander Hudson

    • Rigetti Quantum Computing

  • Michael Selvanayagam

    • Rigetti Quantum Computing

  • Andrew Bestwick

    • Rigetti Quantum Computing

  • Matthew Reagor

    • Rigetti Quantum Computing

  • Chad Rigetti

    • Rigetti Quantum Computing