A Few Constrain Many: Correlation-Enhanced Learning of Many-Body Quantum Systems
Abstract
Discovering properties of many-body quantum systems is challenging because of the large number of parameters to determine. Here, we show that correlations among quantum observables help reduce the complexity of quantum learning. A polynomial number of selected measurements can bound the values of exponentially many dependent yet unmeasured quantities, enabling efficient estimation of general functions of quantum states. We leverage this result to design correlation-informed learning algorithms that estimate key quantum resources, such as entanglement and magic, in systems of 100 qubits. They achieve a lower relative error than shadow tomography and neural-state methods by using information about a polynomial number of measured Pauli strings. These protocols are readily testable with today's quantum computers, as they do not require premeasurement entangling gates. Quantum constraints themselves are therefore a resource for exploring large quantum systems.
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BibTeX
@misc{tedde2026constrain,
title = {{A Few Constrain Many: Correlation-Enhanced Learning of Many-Body Quantum Systems}},
author = {Matteo Tedde and Davide Girolami},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.39935},
eprint = {2609.39935},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.39935},
}
RIS
TY - UNPB
TI - A Few Constrain Many: Correlation-Enhanced Learning of Many-Body Quantum Systems
AU - Matteo Tedde
AU - Davide Girolami
PY - 2026
DA - 2026/09/30
PB - arXiv
DO - 10.48550/arxiv.2609.39935
UR - https://arxiv.org/abs/2609.39935
AB - Discovering properties of many-body quantum systems is challenging because of the large number of parameters to determine. Here, we show that correlations among quantum observables help reduce the complexity of quantum learning. A polynomial number of selected measurements can bound the values of exponentially many dependent yet unmeasured quantities, enabling efficient estimation of general functions of quantum states. We leverage this result to design correlation-informed learning algorithms that estimate key quantum resources, such as entanglement and magic, in systems of 100 qubits. They achieve a lower relative error than shadow tomography and neural-state methods by using information about a polynomial number of measured Pauli strings. These protocols are readily testable with today's quantum computers, as they do not require premeasurement entangling gates. Quantum constraints themselves are therefore a resource for exploring large quantum systems.
ER -