Reliable Optimization Under Noise in Quantum Variational Algorithms

• URL: http://arxiv.org/abs/2511.08289v1
• Date: Wed, 12 Nov 2025 01:51:04 GMT
• Title: Reliable Optimization Under Noise in Quantum Variational Algorithms
• Authors: Vojtěch Novák, Silvie Illésová, Tomáš Bezděk, Ivan Zelinka, Martin Beseda,
• Abstract summary: We show that Variational Quantum Eigensolver is severely challenged by finite-shot sampling noise.<n>We identify adaptive metaheuristics as the most effective and resilient strategies.
• Score: 0.05219568203653522
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The optimization of Variational Quantum Eigensolver is severely challenged by finite-shot sampling noise, which distorts the cost landscape, creates false variational minima, and induces statistical bias called winner's curse. We investigate this phenomenon by benchmarking eight classical optimizers spanning gradient-based, gradient-free, and metaheuristic methods on quantum chemistry Hamiltonians H$_2$, H$_4$ chain, LiH (in both full and active spaces) using the truncated Variational Hamiltonian Ansatz. We analyze difficulties of gradient-based methods (e.g., SLSQP, BFGS) in noisy regimes, where they diverge or stagnate. We show that the bias of estimator can be corrected by tracking the \textit{population mean}, rather than the biased best individual when using population based optimizer. Our findings, which are shown to generalize to hardware-efficient circuits and condensed matter models, identify adaptive metaheuristics (specifically CMA-ES and iL-SHADE) as the most effective and resilient strategies. We conclude by presenting a set of practical guidelines for reliable VQE optimization under noise, centering on the co-design of physically motivated ansatz and the use of adaptive optimizers.

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