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.
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BibTeX
@misc{theißinger2026measurementefficient,
title = {{Measurement-Efficient Differentiable Quantum Architecture Search for Combinatorial Optimization}},
author = {Lukas Theißinger and Thore Gerlach and Christian Bauckhage},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.10351},
eprint = {2610.10351},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.10351},
}
RIS
TY - UNPB
TI - Measurement-Efficient Differentiable Quantum Architecture Search for Combinatorial Optimization
AU - Lukas Theißinger
AU - Thore Gerlach
AU - Christian Bauckhage
PY - 2026
DA - 2026/10/07
PB - arXiv
DO - 10.48550/arxiv.2610.10351
UR - https://arxiv.org/abs/2610.10351
AB - 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.
ER -