Reinforcement learning-based architecture search for quantum machine learning

TL;DR

This paper introduces a reinforcement learning framework to automatically design problem-specific quantum encoding circuits, improving quantum machine learning performance while being sample-efficient and considering hardware constraints.

Contribution

It presents a novel reinforcement learning-based method for generating tailored quantum encoding circuits, reducing search complexity and incorporating multiple optimization objectives.

Findings

Tailored circuits outperform problem-agnostic models in QML tasks.

The method reduces the number of circuit evaluations needed during search.

Problem-specific circuits enhance quantum machine learning model performance.

Abstract

Quantum machine learning models use encoding circuits to map data into a quantum Hilbert space. While it is well known that the architecture of these circuits significantly influences core properties of the resulting model, they are often chosen heuristically. In this work, we present a novel approach using reinforcement learning techniques to generate problem-specific encoding circuits to improve the performance of quantum machine learning models. By specifically using a model-based reinforcement learning algorithm, we reduce the number of necessary circuit evaluations during the search, providing a sample-efficient framework. In contrast to previous search algorithms, our method uses a layered circuit structure that significantly reduces the search space. Additionally, our approach can account for multiple objectives such as solution quality, hardware restrictions and circuit depth. We benchmark our tailored circuits against various reference models, including models with problem-agnostic circuits and classical models. Our results highlight the effectiveness of problem-specific encoding circuits in enhancing QML model performance.