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A Novel Noise-Aware Classical Optimizer for Variational Quantum Algorithms

Journal Article · · INFORMS Journal on Computing
 [1];  [1];  [2]
  1. Argonne National Laboratory (ANL), Argonne, IL (United States)
  2. Univ. of Michigan, Ann Arbor, MI (United States)
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A key component of variational quantum algorithms (VQAs) is the choice of classical optimizer employed to update the parameterization of an ansatz. It is well recognized that quantum algorithms will, for the foreseeable future, necessarily be run on noisy devices with limited fidelities. Thus, the evaluation of an objective function (e.g., the guiding function in the quantum approximate optimization algorithm (QAOA) or the expectation of the electronic Hamiltonian in variational quantum eigensolver (VQE)) required by a classical optimizer is subject not only to stochastic error from estimating an expected value but also to error resulting from intermittent hardware noise. Model-based derivative-free optimization methods have emerged as popular choices of a classical optimizer in the noisy VQA setting, based on empirical studies. However, these optimization methods were not explicitly designed with the consideration of noise. In this work we adapt recent developments from the “noise-aware numerical optimization” literature to these commonly used derivative-free model-based methods. We introduce the key defining characteristics of these novel noise-aware derivative-free model-based methods that separate them from standard model-based methods. In conclusion, we study an implementation of such noise-aware derivative-free model-based methods and compare its performance on demonstrative VQA simulations to classical solvers packaged in scikit-quant.
Research Organization:
Argonne National Laboratory (ANL), Argonne, IL (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)
Grant/Contract Number:
AC02-06CH11357
OSTI ID:
2588445
Journal Information:
INFORMS Journal on Computing, Journal Name: INFORMS Journal on Computing Journal Issue: 1 Vol. 37; ISSN 1526-5528; ISSN 1091-9856
Publisher:
Institute for Operations Research and the Management Sciences (INFORMS)Copyright Statement
Country of Publication:
United States
Language:
English

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Related Subjects

classical optimizers
derivative-free optimization
noisy optimization
optimization methods
variational quantum algorithms