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DOE OSTI · 2583843

Quantifying the impact of precision errors on quantum approximate optimization algorithms

Abstract

The quantum approximate optimization algorithm (QAOA) is a hybrid quantum-classical algorithm that seeks to achieve approximate solutions to optimization problems by iteratively alternating between intervals of controlled quantum evolution. Here, we examine the effect of analog precision errors on QAOA performance from the perspective of both algorithmic training and performance guarantees. Leveraging cumulant expansions, we recast the faulty QAOA as a control problem in which precision errors are expressed as multiplicative control noise and derive bounds on the performance of QAOA. We show using both analytical techniques and numerical simulations that fixed precision implementations of QAOA circuits are subject to an exponential degradation in performance dependent upon the number of optimal QAOA layers and magnitude of the precision error. Despite this significant reduction, we show that it is possible to mitigate precision errors in QAOA via digitization of the variational parameters at the cost of increasing circuit depth.

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BibTeXRIS

Quiroz, Gregory [Johns Hopkins Univ., Baltimore, MD (United States)] (ORCID:0000000343291445), Titum, Paraj [Johns Hopkins Univ., Baltimore, MD (United States)] (ORCID:0000000277921532), Lotshaw, Phillip [Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)] (ORCID:0000000275942735), Lougovski, Pavel [Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)] (ORCID:0000000292291444), Schultz, Kevin [Johns Hopkins Univ., Baltimore, MD (United States)] (ORCID:0000000226646227), Dumitrescu, Eugene [Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)] (ORCID:0000000158519567), Hen, Itay [Univ. of Southern California, Los Angeles, CA (United States); Univ. of Southern California, Marina del Rey, CA (United States)] (ORCID:0000000270097739). 2025-06-09. Quantifying the impact of precision errors on quantum approximate optimization algorithms. https://doi.org/10.1103/physrevresearch.7.023240

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