Problem
Research questions and friction points this paper is trying to address.
Differentiable Quantum Architecture Search
Combinatorial Optimization
Measurement Cost
Quantum Hardware
Measurement-Efficient Differentiable Quantum Architecture Search for Combinatorial Optimization
🤖 AI Summary
This study addresses the prohibitive measurement overhead of gradient estimation that impedes the hardware deployment of Differentiable Quantum Architecture Search (DQAS). For combinatorial optimization tasks, this work proposes a measurement-efficient framework leveraging rotation-gate parameterization and classical post-processing. Through rigorous theoretical derivation, the proposed approach structurally reduces measurement costs without altering the original optimization objective. Experimental evaluations on 3-SAT and MaxCut benchmarks demonstrate that this method decreases the number of circuit measurements required for gradient estimation by approximately 40% while introducing only negligible classical computational overhead. Consequently, it effectively overcomes the hardware execution bottleneck inherent in DQAS, offering a practical pathway for scalable quantum architecture search on near-term quantum devices.
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Differentiable Quantum Architecture Search
Combinatorial Optimization
Measurement Reduction
Variational Quantum Algorithms
Quantum Circuit Design
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