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.
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Presenters
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William J Huggins
Google Quantum AI, Google LLC
Authors
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William J Huggins
Google Quantum AI, Google LLC
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Nathan Wiebe
University of Toronto, University of Toronto & Pacific Northwest National Lab
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Jarrod McClean
Google Quantum AI, Google LLC
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Thomas E O'Brien
Google Quantum AI, Google LLC
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Kianna Wan
Google Quantum AI & Stanford Institute for Theoretical Physics
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Ryan Babbush
Google Quantum AI, Google LLC