Topology-Driven Quantum Architecture Search Framework

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.