Denoising Gradient Descent in Variational Quantum Algorithms

Lars Simon; Holger Eble; Hagen-Henrik Kowalski; Manuel Radons

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arXiv · 2403.03826

Denoising Gradient Descent in Variational Quantum Algorithms

Lars Simon·Holger Eble·Hagen-Henrik Kowalski·Manuel Radons

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Abstract

In this article we introduce an algorithm for mitigating the adverse effects of noise on gradient descent in variational quantum algorithms. This is accomplished by computing a {\emph{regularized}} local classical approximation to the objective function at every gradient descent step. The computational overhead of our algorithm is entirely classical, i.e., the number of circuit evaluations is exactly the same as when carrying out gradient descent using the parameter-shift rules. We empirically demonstrate the advantages offered by our algorithm on randomized parametrized quantum circuits.

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Lars Simon, Holger Eble, Hagen-Henrik Kowalski, Manuel Radons. 2024-03-06. Denoising Gradient Descent in Variational Quantum Algorithms. https://arxiv.org/abs/2403.03826

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