Quantum circuit architecture search for variational quantum algorithms

TL;DR

This paper introduces quantum architecture search (QAS), an efficient method to automatically find near-optimal quantum circuit architectures for variational quantum algorithms, improving robustness and performance on noisy quantum devices.

Contribution

The paper proposes QAS, a novel automated approach to optimize quantum circuit architectures, balancing expressivity and noise effects for better VQA performance.

Findings

QAS alleviates quantum noise effects

QAS outperforms pre-selected ansatze

QAS improves VQA trainability and robustness

Abstract

Variational quantum algorithms (VQAs) are expected to be a path to quantum advantages on noisy intermediate-scale quantum devices. However, both empirical and theoretical results exhibit that the deployed ansatz heavily affects the performance of VQAs such that an ansatz with a larger number of quantum gates enables a stronger expressivity, while the accumulated noise may render a poor trainability. To maximally improve the robustness and trainability of VQAs, here we devise a resource and runtime efficient scheme termed quantum architecture search (QAS). In particular, given a learning task, QAS automatically seeks a near-optimal ansatz (i.e., circuit architecture) to balance benefits and side-effects brought by adding more noisy quantum gates to achieve a good performance. We implement QAS on both the numerical simulator and real quantum hardware, via the IBM cloud, to accomplish data classification and quantum chemistry tasks. In the problems studied, numerical and experimental results show that QAS can not only alleviate the influence of quantum noise and barren plateaus, but also outperforms VQAs with pre-selected ansatze.