Benchmarking Quantum Architecture Search with Surrogate Assistance

TL;DR

This paper introduces SQuASH, a surrogate-assisted benchmarking framework for quantum architecture search that accelerates evaluation and comparison of different QAS methods, promoting reproducibility and rapid prototyping.

Contribution

The paper presents a novel surrogate benchmark for quantum architecture search, enabling faster evaluation and comparison of QAS methods with an open-source toolkit and dataset.

Findings

SQuASH significantly reduces evaluation time for QAS methods.

The surrogate models maintain high accuracy in predicting QAS performance.

The framework promotes reproducibility and facilitates rapid prototyping of QAS algorithms.

Abstract

The development of quantum algorithms and their practical applications currently relies heavily on the efficient design, compilation, and optimization of quantum circuits. In particular, parametrized quantum circuits (PQCs), which serve as the basis for variational quantum algorithms~(VQAs), demand carefully engineered architectures that balance performance with hardware constraints. Despite recent progress, identifying structural features of PQCs that enhance trainability, noise resilience, and overall algorithmic performance remains an active area of research. Addressing these challenges, quantum architecture search (QAS) aims to automate the design of problem-specific PQCs by systematically exploring circuit architectures to optimize algorithmic performance, often with varying degrees of consideration for hardware constraints. However, comparing QAS methods is challenging due to the absence of a unified benchmark evaluation pipeline, and the high resource demands. In this paper, we present SQuASH, the Surrogate Quantum Architecture Search Helper, a benchmark that leverages surrogate models to enable uniform comparison of QAS methods and considerably accelerate their evaluation. We present the methodology for creating a surrogate benchmark for QAS and demonstrate its capability to accelerate the execution and comparison of QAS methods. Additionally, we provide the code required to integrate SQuASH into custom QAS methods, enabling not only benchmarking but also the use of surrogate models for rapid prototyping. We further release the dataset used to train the surrogate models, facilitating reproducibility and further research.