Learning ground state observables from quantum computing experiments

2026-06-14

AI Summary

This work investigates how to efficiently learn from and predict ground-state observables of two-dimensional many-body systems using approximate ground-state data generated on noisy quantum processors. Focusing on the Heisenberg XXZ model, we prepare approximate ground states in two-dimensional systems with up to 115 qubits, measure single-site expectation values, two-point correlations, and 12-body loop correlators to construct an experimental dataset, and train neural networks to predict spatially resolved observables for arbitrary Hamiltonian parameters. This approach represents the first demonstration of machine learning based on real quantum data in large-scale two-dimensional interacting systems. The trained models not only achieve high accuracy within the training distribution but also generalize to out-of-distribution regimes near phase boundaries, demonstrating that noisy quantum devices can provide training data beyond the reach of classical simulation capabilities.

Abstract

Recent theoretical progress has established conditions under which machine learning models can efficiently predict ground-state properties of gapped local Hamiltonians when trained on quantum-generated data. Previous experimental demonstrations in this paradigm, however, have largely been limited to small systems or highly structured states, due to the difficulty of preparing many-body ground states on quantum processors. In this work, we demonstrate learning from experimental quantum data generated from approximate ground states of the two-dimensional Heisenberg XXZ model with system sizes up to 115 qubits. We construct a dataset of single-site expectation values, two-point correlations, and 12-body loop correlations across the antiferromagnetic phase. We then train neural networks on this data and show that they can accurately predict spatially resolved observables for previously unseen Hamiltonian parameters, both within the training distribution and in an out-of-distribution regime approaching the phase boundary. Our results demonstrate the practical realization of learning from quantum data for an interacting two-dimensional many-body system at scale, motivating a path toward regimes where quantum processors could provide training data beyond the reach of classical approximation methods.

Problem

• ground state
• quantum computing
• many-body system
• Heisenberg XXZ model
• observables

Innovation

• quantum machine learning
• ground-state observables
• Heisenberg XXZ model
• many-body quantum systems
• out-of-distribution prediction