Optimizing Hamiltonian control using quantum machine learning method

Abstract

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.