Reinforcement learning-based architecture search for quantum machine learning

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.

14 pages, 5 figures, 1 table; Updated authorship, and improved RL section

References in corpus (27)

• XGBoost: A Scalable Tree Boosting System
• Quantum Computing in the NISQ era and beyond
• A variational eigenvalue solver on a quantum processor
• Supervised learning with quantum enhanced feature spaces
• Quantum support vector machine for big data classification
• Quantum machine learning in feature Hilbert spaces
• Quantum Circuit Learning
• Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model
• Parameterized quantum circuits as machine learning models
• The Variational Quantum Eigensolver: a review of methods and best practices
• The effect of data encoding on the expressive power of variational quantum machine learning models
• Power of data in quantum machine learning
• Challenges and Opportunities in Quantum Machine Learning
• Quantum machine learning beyond kernel methods
• Exploiting symmetry in variational quantum machine learning
• Differentiable Quantum Architecture Search
• Group-Invariant Quantum Machine Learning
• A Lie Algebraic Theory of Barren Plateaus for Deep Parameterized Quantum Circuits
• Theory for Equivariant Quantum Neural Networks
• Theoretical Guarantees for Permutation-Equivariant Quantum Neural Networks
• Quantum circuit optimization with deep reinforcement learning
• Automatic design of quantum feature maps
• Covariant quantum kernels for data with group structure
• Quantum machine learning of large datasets using randomized measurements
• Reduction of finite sampling noise in quantum neural networks
• Automatic and effective discovery of quantum kernels
• A quantum information theoretic analysis of reinforcement learning-assisted quantum architecture search

Cited by in corpus (2)

• Quantum Kernel Methods under Scrutiny: A Benchmarking Study
• On the similarity of bandwidth-tuned quantum kernels and classical kernels