A Novel Noise-Aware Classical Optimizer for Variational Quantum Algorithms

• URL: http://arxiv.org/abs/2401.10121v2
• Date: Fri, 14 Jun 2024 16:47:14 GMT
• Title: A Novel Noise-Aware Classical Optimizer for Variational Quantum Algorithms
• Authors: Jeffrey Larson, Matt Menickelly, Jiahao Shi,
• Abstract summary: A key component of variational quantum algorithms (VQAs) is the choice of classical solvers 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. We introduce the key defining characteristics of novel noise-aware derivative-free model-based methods that separate them from standard model-based methods.
• Score: 1.4770642357768933
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. 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 \texttt{scikit-quant}.

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