Electronic-Structure-Informed Many-Body Encoding for Quantum Machine Learning
Abstract
Data encoding is a central challenge in quantum machine learning (QML), as it determines how classical information is mapped onto quantum states and influences the expressive power and efficiency of parameterized quantum circuits. Here, we introduce a physics-informed quantum encoding strategy that exploits the intrinsic many-body structure of electronic systems to establish a direct correspondence between the body order of input descriptors and the locality of quantum gates. Specifically, n-body electronic descriptors obtained from a single mean-field electronic-structure calculation are encoded through n-qubit gates, yielding a systematic hierarchy of quantum circuits with increasing many-body expressivity. Using reduced-density-matrix descriptors, we construct quantum machine-learning models incorporating one- through four-body encodings and benchmark their performance in predicting electron-correlation energies along bond-stretching potential energy surfaces. We demonstrate that increasing the body order of the encoding systematically improves prediction accuracy toward the full configuration interaction limit. Remarkably, disconnected many-particle densities constructed from lower-order electronic information achieve accuracy comparable to explicitly correlated two-particle densities in key cases, providing a computationally inexpensive route to higher-body quantum encodings. These findings establish a connection between the many-body structure of classical data and the design of quantum feature maps, offering a systematic, physically interpretable framework for enhancing the expressivity and predictive performance of quantum machine-learning circuits.
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BibTeX
@misc{zhang2026electronicstructureinformed,
title = {{Electronic-Structure-Informed Many-Body Encoding for Quantum Machine Learning}},
author = {Liangnuo Zhang and Tanner Culpitt and Qiwei Yin and Linghua Zhu and Xiaosong Li},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.40840},
eprint = {2610.40840},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.40840},
}
RIS
TY - UNPB
TI - Electronic-Structure-Informed Many-Body Encoding for Quantum Machine Learning
AU - Liangnuo Zhang
AU - Tanner Culpitt
AU - Qiwei Yin
AU - Linghua Zhu
AU - Xiaosong Li
PY - 2026
DA - 2026/10/29
PB - arXiv
DO - 10.48550/arxiv.2610.40840
UR - https://arxiv.org/abs/2610.40840
AB - Data encoding is a central challenge in quantum machine learning (QML), as it determines how classical information is mapped onto quantum states and influences the expressive power and efficiency of parameterized quantum circuits. Here, we introduce a physics-informed quantum encoding strategy that exploits the intrinsic many-body structure of electronic systems to establish a direct correspondence between the body order of input descriptors and the locality of quantum gates. Specifically, n-body electronic descriptors obtained from a single mean-field electronic-structure calculation are encoded through n-qubit gates, yielding a systematic hierarchy of quantum circuits with increasing many-body expressivity. Using reduced-density-matrix descriptors, we construct quantum machine-learning models incorporating one- through four-body encodings and benchmark their performance in predicting electron-correlation energies along bond-stretching potential energy surfaces. We demonstrate that increasing the body order of the encoding systematically improves prediction accuracy toward the full configuration interaction limit. Remarkably, disconnected many-particle densities constructed from lower-order electronic information achieve accuracy comparable to explicitly correlated two-particle densities in key cases, providing a computationally inexpensive route to higher-body quantum encodings. These findings establish a connection between the many-body structure of classical data and the design of quantum feature maps, offering a systematic, physically interpretable framework for enhancing the expressivity and predictive performance of quantum machine-learning circuits.
ER -