# Machine learning quantum states in the NISQ era

## TL;DR

This paper reviews machine learning techniques, especially restricted Boltzmann machines, for reconstructing noisy quantum states on NISQ devices, highlighting recent experimental advances and future prospects.

## Contribution

It provides a comprehensive overview of generative modeling for quantum state reconstruction, emphasizing practical applications on NISQ hardware and discussing future directions.

## Key findings

- Successful reconstruction of classical and quantum states using RBMs
- Application to experimental cold atom wavefunctions
- Discussion of future NISQ-era quantum state tomography

## Abstract

We review the development of generative modeling techniques in machine learning for the purpose of reconstructing real, noisy, many-qubit quantum states. Motivated by its interpretability and utility, we discuss in detail the theory of the restricted Boltzmann machine. We demonstrate its practical use for state reconstruction, starting from a classical thermal distribution of Ising spins, then moving systematically through increasingly complex pure and mixed quantum states. Intended for use on experimental noisy intermediate-scale quantum (NISQ) devices, we review recent efforts in reconstruction of a cold atom wavefunction. Finally, we discuss the outlook for future experimental state reconstruction using machine learning, in the NISQ era and beyond.