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Communications and control engineering/Communications and control engineering series

Machine Learning for Quantum Control

Daoyi Dong, Ian R. Petersen

Published Jan 1, 2023• 7 citations
Publisher
DOI
10.1007/978-3-031-20245-2_5

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Machine learning brings the promise of deriving meaning from different data, which is widely used for medicine and computer science. Quantum computing exploits the phenomena of quantum states to realize parallel computing, which believed to be significantly faster than classical computers. Quantum machine learning is the integration of quantum algorithms within machine learning programs, thereby improving classical machine learning techniques. Quantum control such as gate decomposition and Hamiltonian control is an essential problem to solve in quantum dynamics. Here we present a hybrid quantum machine learning model to conduct quantum control optimization. Firstly, according to Bloch theorem, any single-qubit unitary gate can be specified by three vector rotations. We find the optimal decomposition of the given gate, which is the optimal control sequence. Secondly, we build a hybrid quantum-classical neural networks to solve gate decomposition problem utilizing supervised learning. And we prepare the training data and train the hybrid supervised model. Finally, we demonstrate the quantum control optimization results in hybrid model on quantum gates.
The integration of quantum computing with machine learning represents a significant frontier in computational research. This paper introduces an innovative approach that combines quantum control techniques with quantum machine learning (QML) to achieve notable advancements in performance and efficiency. The proposed methodology introduces a new quantum control framework designed to optimize quantum machine learning algorithms. This approach not only enhances computational speed but also improves the accuracy of quantum-enhanced support vector machines (SVMs) for classification tasks. The innovation lies in the application of advanced quantum control techniques, which are demonstrated to offer superior performance compared to existing methods. Additionally, the paper presents novel strategies for reducing power consumption in quantum computing systems. By incorporating dynamic quantum control, the proposed system achieves significant improvements in power efficiency, addressing one of the critical challenges in practical quantum computing applications. Benchmark tests across various datasets validate the effectiveness of the new method, showing advancements in processing efficiency and accuracy. The integration of machine learning creates new opportunities for advancements in research, potentially setting new standards in the field of quantum computing. These innovations underscore the potential for combining quantum control with machine learning to achieve groundbreaking improvements in computational capabilities and system efficiency, offering promising directions for future research and practical implementations.