Efficient Quantum State Tomography with Mode-assisted Training

TL;DR

This paper introduces a mode-assisted training method for neural network-based quantum state tomography, significantly enhancing reconstruction quality by leveraging global information, and demonstrating broad applicability beyond restricted Boltzmann machines.

Contribution

The paper proposes a novel mode-assisted training approach that improves quantum state reconstruction, addressing slow-mixing issues in traditional local-move samplers.

Findings

Reconstruction quality improved by orders of magnitude.

Method applicable to various neural network architectures.

Potential to solve previously intractable quantum tomography problems.

Abstract

Neural networks (NNs) representing quantum states are typically trained using Markov chain Monte Carlo based methods. However, unless specifically designed, such samplers only consist of local moves, making the slow-mixing problem prominent even for extremely simple quantum states. Here, we propose to use mode-assisted training that provides global information via the modes of the NN distribution. Applied to quantum state tomography using restricted Boltzmann machines, this method improves the quality of reconstructed quantum states by orders of magnitude. The method is applicable to other types of NNs and may efficiently tackle problems previously unmanageable.