Jun 14, 2026 · quant-phJ/K move · Enter open · S save
Ben Jaderberg, Freya Shah, Minjun Jeon, M. Emre Sahin+2
IBM Quantum, IBM Research Europe, Hursley, Winchester, SO21 2JN, United Kingdom · Department of Engineering Science, University of Oxford, Parks Road, Oxford OX1 3PJ, United Kingdom · IBM Quantum, T. J. Watson Research Center, Yorktown Heights, NY 10598, USA · Department of Materials, University of Oxford, Parks Road, Oxford OX1 3PH, United Kingdom · The Hartree Centre, STFC, Sci-Tech Daresbury, Warrington WA4 4AD, UK
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.