Physics-Informed Variational Quantum Classifier for Phase Detection in Strongly Correlated Matter
Abstract
The characterisation of quantum phases in strongly correlated systems is a crucial milestone for the deployment of quantum sensors. In this work, we present a Physics-Informed Variational Quantum Classifier (VQC) designed to detect the topological phase transition between the Fermi polaron quasiparticle and the molecular bound state. Unlike conventional Machine Learning approaches, our quantum architecture is constructed via the Trotterised time-evolution of an effective Hamiltonian, ensuring that the learnable parameters correspond to interpretable physical quantities. We show that the VQC efficiently discovers the optimal interferometric protocol, specifically the evolution time and effective bath interactions required to maximise the visibility of Ramsey fringes, thereby clearly distinguishing the Bose-Einstein Condensate (BEC) and Bardeen-Cooper-Schrieffer (BCS) regimes. Furthermore, we report the validation of this classifier on the QRed superconducting quantum processor (BSC-CNS). Despite the intrinsic hardware noise and decoherence, the VQC preserves the relative ordering of the topological phases. We demonstrate that the physics-informed architecture achieves a linear gate complexity 𝒪(N), bypassing the exponential memory wall of classical simulation and ensuring scalability to many-body regimes.
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BibTeX
@misc{catala2026physicsinformed,
title = {{Physics-Informed Variational Quantum Classifier for Phase Detection in Strongly Correlated Matter}},
author = {Hugo Catalá and Ezequiel Valero and Germán Rodrigo},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2606.14489},
eprint = {2606.14489},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2606.14489},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Physics-Informed Variational Quantum Classifier for Phase Detection in Strongly Correlated Matter
AU - Hugo Catalá
AU - Ezequiel Valero
AU - Germán Rodrigo
PY - 2026
DA - 2026/09/22
PB - arXiv
DO - 10.48550/arxiv.2606.14489
UR - https://arxiv.org/abs/2606.14489
AB - The characterisation of quantum phases in strongly correlated systems is a crucial milestone for the deployment of quantum sensors. In this work, we present a Physics-Informed Variational Quantum Classifier (VQC) designed to detect the topological phase transition between the Fermi polaron quasiparticle and the molecular bound state. Unlike conventional Machine Learning approaches, our quantum architecture is constructed via the Trotterised time-evolution of an effective Hamiltonian, ensuring that the learnable parameters correspond to interpretable physical quantities. We show that the VQC efficiently discovers the optimal interferometric protocol, specifically the evolution time and effective bath interactions required to maximise the visibility of Ramsey fringes, thereby clearly distinguishing the Bose-Einstein Condensate (BEC) and Bardeen-Cooper-Schrieffer (BCS) regimes. Furthermore, we report the validation of this classifier on the QRed superconducting quantum processor (BSC-CNS). Despite the intrinsic hardware noise and decoherence, the VQC preserves the relative ordering of the topological phases. We demonstrate that the physics-informed architecture achieves a linear gate complexity 𝒪(N), bypassing the exponential memory wall of classical simulation and ensuring scalability to many-body regimes.
ER -