Neural Predictor based Quantum Architecture Search

TL;DR

This paper introduces a neural predictor-guided quantum architecture search method that efficiently discovers high-performance parameterized quantum circuits, outperforming random search and generalizing across similar problems.

Contribution

It proposes a neural network based predictor to guide quantum architecture search, achieving state-of-the-art results with fewer evaluations and better transferability.

Findings

Neural predictor guided QAS outperforms random search in discovering PQCs.

The method requires an order of magnitude fewer circuit evaluations.

The predictor and optimal ansatz can be transferred to similar problems.

Abstract

Variational quantum algorithms (VQAs) are widely speculated to deliver quantum advantages for practical problems under the quantum-classical hybrid computational paradigm in the near term. Both theoretical and practical developments of VQAs share many similarities with those of deep learning. For instance, a key component of VQAs is the design of task-dependent parameterized quantum circuits (PQCs) as in the case of designing a good neural architecture in deep learning. Partly inspired by the recent success of AutoML and neural architecture search (NAS), quantum architecture search (QAS) is a collection of methods devised to engineer an optimal task-specific PQC. It has been proven that QAS-designed VQAs can outperform expert-crafted VQAs under various scenarios. In this work, we propose to use a neural network based predictor as the evaluation policy for QAS. We demonstrate a neural predictor guided QAS can discover powerful PQCs, yielding state-of-the-art results for various examples from quantum simulation and quantum machine learning. Notably, neural predictor guided QAS provides a better solution than that by the random-search baseline while using an order of magnitude less of circuit evaluations. Moreover, the predictor for QAS as well as the optimal ansatz found by QAS can both be transferred and generalized to address similar problems.