Reinforcement learning-based architecture search for quantum machine learning
This work addresses the need for efficient and tailored circuit design in quantum machine learning, offering a sample-efficient method that is incremental over prior search algorithms.
The paper tackled the problem of heuristic selection of encoding circuits in quantum machine learning by using reinforcement learning to generate problem-specific circuits, resulting in improved model performance as benchmarked against reference models.
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.