Unlearning-driven Quantum Architecture Search

Quantum machine unlearning aims to remove the influence of specified training data from quantum models. However, existing quantum architecture search (QAS) primarily selects architectures based on predictive accuracy and does not account for potential post-deployment unlearning needs. To bridge this research gap, we conduct an empirical study and reveal for the first time that quantum model architecture itself can substantially affect unlearning capability, even among architectures with comparable predictive accuracy. Building on this observation, we propose Unlearning-driven Quantum Architecture Search (UQAS), a framework that incorporates unlearning capability into QAS to better prepare quantum models for post-deployment unlearning requests. In UQAS, we first introduce an Unlearning Capability Evaluator (UCE) to score the unlearning capability of a quantum architecture using synthetic unlearning requests. Then we design two complementary algorithms, UQAS-Select and UQAS-Design, to identify the quantum architecture with the best UCE score under two QAS settings (with and without an existing architecture pool) respectively. To evaluate the performance of the proposed UQAS, we conduct extensive experiments in both simulation and on IBM quantum computers, across sixteen evaluation scenarios spanning four diverse datasets and four accuracy-based QAS methods. The performance analysis reveals that UQAS effectively identifies quantum architectures with stronger unlearning capability while maintaining competitive predictive accuracy. Furthermore, UQAS-Select and UQAS-Design demonstrate complementary trade-offs between search cost and unlearning capabilities, enabling flexible selection under different computational budgets.