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PreprintarXiv licencearXiv

Adiabatic training of VQAs for entanglement detection

  • E. Acosta
  • C. Cano
  • D. Manzano
  • G. Botella
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Abstract

This paper introduces a hybrid quantum framework for training Variational Quantum Algorithms (VQAs) aimed at addressing the fundamental problem of entanglement detection using adiabatic quantum computing. This problem is of foundational significance in quantum information science and has been approached through both classical and quantum methodologies. We propose an adiabatic-inspired optimization strategy for training VQAs, which combines a discretization of the parameter space with a hierarchical segmentation scheme. The optimization problem is formulated as a Quadratic Unconstrained Binary Optimization (QUBO) model to leverage the capabilities of quantum annealing. Preliminary results are compared against classical neural networks and conventional VQA training techniques. The findings aim to stimulate further investigation and discussion within the scientific community regarding the efficacy and applicability of the proposed approach.

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Cite

BibTeXRIS
BibTeX
@misc{acosta2026adiabatic,
  title = {{Adiabatic training of VQAs for entanglement detection}},
  author = {E. Acosta and C. Cano and D. Manzano and G. Botella},
  howpublished = {arXiv},
  year = {2026},
  month = oct,
  doi = {10.48550/arxiv.2610.39577},
  eprint = {2610.39577},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2610.39577},
}
RIS
TY  - UNPB
TI  - Adiabatic training of VQAs for entanglement detection
AU  - E. Acosta
AU  - C. Cano
AU  - D. Manzano
AU  - G. Botella
PY  - 2026
DA  - 2026/10/29
PB  - arXiv
DO  - 10.48550/arxiv.2610.39577
UR  - https://arxiv.org/abs/2610.39577
AB  - This paper introduces a hybrid quantum framework for training Variational Quantum Algorithms (VQAs) aimed at addressing the fundamental problem of entanglement detection using adiabatic quantum computing. This problem is of foundational significance in quantum information science and has been approached through both classical and quantum methodologies. We propose an adiabatic-inspired optimization strategy for training VQAs, which combines a discretization of the parameter space with a hierarchical segmentation scheme. The optimization problem is formulated as a Quadratic Unconstrained Binary Optimization (QUBO) model to leverage the capabilities of quantum annealing. Preliminary results are compared against classical neural networks and conventional VQA training techniques. The findings aim to stimulate further investigation and discussion within the scientific community regarding the efficacy and applicability of the proposed approach.
ER  -