Quantum Architecture Search with Meta-learning

TL;DR

This paper introduces MetaQAS, a meta-learning approach for quantum architecture search that enables rapid adaptation of quantum circuit designs to new tasks, significantly improving efficiency and performance over existing methods.

Contribution

MetaQAS learns initial architecture heuristics and gate parameters from multiple tasks, allowing quick adaptation and superior results compared to traditional quantum architecture search algorithms.

Findings

MetaQAS converges faster than DQAS in quantum compiling tasks.

MetaQAS achieves better solutions after fine-tuning.

MetaQAS is compatible with various gradient-based QAS algorithms.

Abstract

Variational quantum algorithms (VQAs) have been successfully applied to quantum approximate optimization algorithms, variational quantum compiling and quantum machine learning models. The performances of VQAs largely depend on the architecture of parameterized quantum circuits (PQCs). Quantum architecture search (QAS) aims to automate the design of PQCs in different VQAs with classical optimization algorithms. However, current QAS algorithms do not use prior experiences and search the quantum architecture from scratch for each new task, which is inefficient and time consuming. In this paper, we propose a meta quantum architecture search (MetaQAS) algorithm, which learns good initialization heuristics of the architecture (i.e., meta-architecture), along with the meta-parameters of quantum gates from a number of training tasks such that they can adapt to new tasks with a small number of gradient updates, which leads to fast learning on new tasks. The proposed MetaQAS can be used with arbitrary gradient-based QAS algorithms. Simulation results of variational quantum compiling on three- and four-qubit circuits show that the architectures optimized by MetaQAS converge much faster than a state-of-the-art gradient-based QAS algorithm, namely DQAS. MetaQAS also achieves a better solution than DQAS after fine-tuning of gate parameters.