Neural network enhanced measurement efficiency for molecular groundstates

TL;DR

This paper demonstrates that neural network models can significantly improve the efficiency of measuring molecular groundstates on quantum computers, reducing the number of measurements needed for accurate energy estimation.

Contribution

The authors adapt neural network models to learn molecular groundstates from measurement data, achieving better scaling in measurement efficiency compared to classical methods.

Findings

Neural networks improve groundstate energy estimation accuracy.

Model-based approaches scale near ε^{-1} in measurements, better than classical shadow tomography.

Neural networks provide robust improvements over single-measurement methods.

Abstract

It is believed that one of the first useful applications for a quantum computer will be the preparation of groundstates of molecular Hamiltonians. A crucial task involving state preparation and readout is obtaining physical observables of such states, which are typically estimated using projective measurements on the qubits. At present, measurement data is costly and time-consuming to obtain on any quantum computing architecture, which has significant consequences for the statistical errors of estimators. In this paper, we adapt common neural network models (restricted Boltzmann machines and recurrent neural networks) to learn complex groundstate wavefunctions for several prototypical molecular qubit Hamiltonians from typical measurement data. By relating the accuracy $\varepsilon$ of the reconstructed groundstate energy to the number of measurements, we find that using a neural network model provides a robust improvement over using single-copy measurement outcomes alone to reconstruct observables. This enhancement yields an asymptotic scaling near $\varepsilon^{-1}$ for the model-based approaches, as opposed to $\varepsilon^{-2}$ in the case of classical shadow tomography.