ScholarWorks@숙명여자대학교

초록

Quantum neural architecture search aims to automate the design of parameterized quantum circuits (PQCs) within quantum neural networks (QNNs), where learning performance is highly sensitive to the placement and type of quantum gates. However, applying traditional NAS techniques to quantum circuits presents significant challenges, including barren plateaus, noise accumulation, device-specific constraints, and the high computational cost of independently evaluating all candidate architectures. To address these limitations, this paper proposes Q-RLONAS, a two-stage quantum NAS framework that integrates deep reinforcement learning (DRL) with one-shot NAS to efficiently explore and optimize PQC structures. In the Stage #1, DRL determines the optimal gate placement by leveraging a reward function that considers performance, noise, barren plateaus, and hardware constraints. In the Stage #2, a one-shot NAS method is employed to select effective quantum gate types using weight sharing within a supernet, significantly reducing training overhead. Experimental results on the mini-MNIST dataset demonstrate that Q-RLONAS achieves high accuracy while satisfying gate placement constraints, outperforming existing approaches in terms of efficiency for quantum circuit design.

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