Evolutionary-based quantum architecture search

TL;DR

This paper introduces an evolutionary algorithm for quantum architecture search that optimizes quantum circuit layouts to improve accuracy and efficiency in quantum machine learning tasks within NISQ devices.

Contribution

The paper presents a novel evolutionary-based quantum architecture search method that encodes circuit layouts, removes redundant parameters, and effectively finds optimal architectures for quantum classification.

Findings

EQAS searches for optimal quantum circuit architectures with fewer gates.

Higher classification accuracies achieved using EQAS on three datasets.

Efficient parameter reduction in quantum circuits through eigenvalue-based pruning.

Abstract

Quantum architecture search (QAS) is desired to construct a powerful and general QAS platform which can significantly accelerate quantum advantages in error-prone and depth limited quantum circuits in today Noisy Intermediate-Scale Quantum (NISQ) era. In this paper, we propose an evolutionary-based quantum architecture search (EQAS) scheme for the optimal layout to balance the higher expressive power and the trainable ability. In EQAS, each layout of quantum circuits, i.e quantum circuit architecture(QCA), is first encoded into a binary string, which is called quantum genes later. Then, an algorithm to remove the redundant parameters in QCA is performed according to the eigenvalues of the corresponding quantum Fisher information matrix (QFIM). Later, each QCA is evaluated by the normalized fitness, so that the sampling rate could be obtained to sample the parent generation by the Roulette Wheel selection strategy. Thereafter, the mutation and crossover are applied to get the next generation. EQAS is verified by the classification task in quantum machine learning for three datasets. The results show that the proposed EQAS can search for the optimal QCA with less parameterized gates, and the higher accuracies are obtained by adopting EQAS for the classification tasks over three dataset.