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Nearly Optimal Quantum Algorithm for Estimating Multiple Expectation Values

ORAL

Abstract

Many quantum algorithms involve the evaluation of expectation values with respect to some pure state. Optimal strategies for estimating a single expectation value to within a precision ε are known, requiring a number of calls to a state preparation oracle proportional to ε-1 in the asymptotic limit. In this paper, we address the task of evaluating the expectation values of M different observables with the same ε-1 scaling in the desired precision. We provide an approach that requires a number of calls to oracle calls that scales as M1/2ε-1(neglecting logarithmic factors). Furthermore, we show that this scaling is optimal, even in the special case when the operators in question commute.

Presenters

  • William J Huggins

    Google Quantum AI, Google LLC

Authors

  • William J Huggins

    Google Quantum AI, Google LLC

  • Nathan Wiebe

    University of Toronto, University of Toronto & Pacific Northwest National Lab

  • Jarrod McClean

    Google Quantum AI, Google LLC

  • Thomas E O'Brien

    Google Quantum AI, Google LLC

  • Kianna Wan

    Google Quantum AI & Stanford Institute for Theoretical Physics

  • Ryan Babbush

    Google Quantum AI, Google LLC