Preparation of Entangled Many-Body States with Machine Learning

Understand

Preparation of a target quantum many-body state on quantum simulators is one of the significant steps in quantum science and technology.

• With a small number of qubits, a few quantum states, such as the Greenberger-Horne-Zeilinger state, have been prepared, but fundamental difficulties in systems with many qubits remain, including the Lieb-Robinson bounds for the number of quantum operations.
• Here, we provide one algorithm with an implementation of a deep learning process and achieve to prepare the target ground states with many qubits.
• Our strategy is to train a machine-learning model and predict parameters with many qubits by utilizing a pattern of quantum states from the corresponding quantum states with small numbers of qubits.