Topology-Driven Quantum Architecture Search Framework

• URL: http://arxiv.org/abs/2502.14265v2
• Date: Mon, 16 Jun 2025 03:21:15 GMT
• Title: Topology-Driven Quantum Architecture Search Framework
• Authors: Junjian Su, Jiacheng Fan, Shengyao Wu, Guanghui Li, Sujuan Qin, Fei Gao,
• Abstract summary: Topology-Driven Quantum Architecture Search (TD-QAS)<n>Topology-Driven Quantum Architecture Search (TD-QAS)<n>Topology-Driven Quantum Architecture Search (TD-QAS)
• Score: 2.9862856321580895
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The limitations of Noisy Intermediate-Scale Quantum (NISQ) devices have motivated the development of Variational Quantum Algorithms (VQAs), which are designed to potentially achieve quantum advantage for specific tasks. Quantum Architecture Search (QAS) algorithms play a critical role in automating the design of high-performance Parameterized Quantum Circuits (PQCs) for VQAs. However, existing QAS approaches struggle with large search spaces, leading to substantial computational overhead when optimizing large-scale quantum circuits. Extensive empirical analysis reveals that circuit topology has a greater impact on quantum circuit performance than gate types. Based on this insight, we propose the Topology-Driven Quantum Architecture Search (TD-QAS) framework, which first identifies optimal circuit topologies and then fine-tunes the gate types. In the fine-tuning phase, the QAS inherits parameters from the topology search phase, eliminating the need for training from scratch. By decoupling the large search space into separate topology and gate-type components, TD-QAS avoids exploring gate configurations within low-performance topologies, thereby significantly reducing computational complexity. Numerical simulations across various tasks, under both noiseless and noisy conditions, validate the effectiveness of the TD-QAS framework. This framework advances standard QAS algorithms by enabling the identification of high-performance quantum circuits while minimizing computational demands. These findings indicate that TD-QAS deepens our understanding of VQAs and offers broad potential for the development of future QAS algorithms.

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