Probabilistic Design of Parametrized Quantum Circuits through Local Gate Modifications

TL;DR

This paper introduces a local quantum architecture search algorithm that optimizes parametrized quantum circuits through a probabilistic, local modification process, demonstrating its effectiveness on regression tasks and quantum chemistry datasets.

Contribution

It presents a novel heuristic quantum architecture search method that automates circuit design via local gate modifications, improving performance on specific tasks.

Findings

Effective in identifying competitive circuit architectures

Applicable to quantum chemistry datasets

Deployable on quantum hardware

Abstract

Within quantum machine learning, parametrized quantum circuits provide flexible quantum models, but their performance is often highly task-dependent, making manual circuit design challenging. Alternatively, quantum architecture search algorithms have been proposed to automate the discovery of task-specific parametrized quantum circuits using systematic frameworks. In this work, we propose an evolution-inspired heuristic quantum architecture search algorithm, which we refer to as the local quantum architecture search. The goal of the local quantum architecture search algorithm is to optimize parametrized quantum circuit architectures through a local, probabilistic search over a fixed set of gate-level actions applied to existing circuits. We evaluate the local quantum architecture search algorithm on two synthetic function-fitting regression tasks and two quantum chemistry regression datasets, including the BSE49 dataset of bond separation energies for first- and second-row elements and a dataset of water conformers generated using the data-driven coupled-cluster approach. Using state-vector simulation, our results highlight the applicability of local quantum architecture search algorithm for identifying competitive circuit architectures with desirable performance metrics. Lastly, we analyze the properties of the discovered circuits and demonstrate the deployment of the best-performing model on state-of-the-art quantum hardware.