Preparation of Entangled Many-Body States with Machine Learning

TL;DR

This paper presents a machine learning algorithm that efficiently prepares entangled many-body quantum states on simulators, scaling to larger qubit systems by leveraging patterns learned from smaller systems.

Contribution

It introduces a deep learning-based method to predict parameters for preparing ground states of large quantum systems, overcoming fundamental quantum operation limits.

Findings

Successfully prepared ground states for a 64-qubit 1D XY model.

Utilized reduced density operators to analyze quantum correlations.

Demonstrated scalability of the machine learning approach.

Abstract

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. For example, we demonstrate that our algorithm with the Quantum Approximate Optimization Ansatz can effectively generate the ground state for a 1D XY model with 64 spins. We also demonstrate that the reduced density operator of two qubits can be utilized to capture the pattern of quantum many-body states such as correlation lengths even for quantum critical states.