Quantum Neural Architecture Search with Quantum Circuits Metric and Bayesian Optimization

TL;DR

This paper introduces a novel quantum circuits metric combined with Bayesian optimization to automatically design quantum neural network architectures, demonstrating superior performance on multiple quantum machine learning tasks.

Contribution

It proposes a new quantum gates distance metric and integrates it with Bayesian optimization for quantum neural architecture search, advancing automated design methods.

Findings

Outperforms benchmarks on quantum generative adversarial networks

Achieves better results on MaxCut combinatorial optimization

Improves quantum Fourier transform simulations

Abstract

Quantum neural networks are promising for a wide range of applications in the Noisy Intermediate-Scale Quantum era. As such, there is an increasing demand for automatic quantum neural architecture search. We tackle this challenge by designing a quantum circuits metric for Bayesian optimization with Gaussian process. To this goal, we propose a new quantum gates distance that characterizes the gates' action over every quantum state and provide a theoretical perspective on its geometrical properties. Our approach significantly outperforms the benchmark on three empirical quantum machine learning problems including training a quantum generative adversarial network, solving combinatorial optimization in the MaxCut problem, and simulating quantum Fourier transform. Our method can be extended to characterize behaviors of various quantum machine learning models.