Measurement-Efficient Differentiable Quantum Architecture Search for Combinatorial Optimization

Abstract:Differentiable quantum architecture search (DQAS) is a promising framework for the automated design of quantum circuits, particularly for variational quantum optimization algorithms. However, its practical deployment on quantum hardware is limited by the large number of circuit measurements required during optimization, making hardware execution costly. In this work, we show that for a broad class of combinatorial optimization problems and commonly used rotational gate parameterizations, the measurement cost of DQAS can be significantly reduced without changing the optimization objective. We derive the proposed measurement reduction scheme theoretically and validate it experimentally on 3-SAT and MaxCut benchmark problems. Our approach reduces the requested gradient measurement cost by about 39 to 41% while introducing only negligible classical post-processing overhead, lowering the practical cost of executing DQAS on quantum hardware.

6 pages, 2 figures. Accepted at the 2026 IEEE 2nd International Conference on Quantum Artificial Intelligence (QAI). Code and data: this https URL

Quantum Physics (quant-ph); Machine Learning (cs.LG)

arXiv:2610.10351 [quant-ph]

(or arXiv:2610.10351v1 [quant-ph] for this version)

https://doi.org/10.48550/arXiv.2610.10351 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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