QAS-Bench: Rethinking Quantum Architecture Search and A Benchmark

Xudong Lu, Kaisen Pan, Ge Yan, Jiaming Shan, Wenjie Wu, Junchi Yan

Abstract: Automatic quantum architecture search (QAS) has been widely studied across disciplines with different implications. In this paper, beyond a particular domain-related QAS task, we formulate the QAS problem into two basic (and relatively even ideal) tasks: i) arbitrary quantum circuit (QC) regeneration given a target QC; ii) approximating an arbitrary unitary (oracle). The latter can be connected to the setting of various quantum machine learning tasks and other QAS applications. Based on these two tasks, we generate a public QAS benchmark which is still missing in literature. We evaluate five baseline algorithms including brute force search, simulated annealing, genetic algorithm, reinforcement learning, and hybrid algorithm as part of our benchmark. One characteristic of our proposed evaluation protocol on the basic tasks is that it deprives the domain specific designs and techniques as used in existing QAS literature, making a unified evaluation possible and focusing on the vanilla search methods themselves without coupling with domain prior. In fact, the unitary approximation task could be algorithmically more difficult than the specific problems as it needs to explore the whole matrix space to fit the unitary. Thus unitary approximation is a more rigorous evaluator for search method. While specific tasks often only need to fit a partial observation of the unitary as the objective for search.