ArticleNPJ quantum information2025
Unveiling quantum phase transitions from traps in variational quantum algorithms.
Article in NPJ quantum information, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed.
- Coherent quantum control of nitrogen vacancy spin with nanoscale magnets.Nature communications · 2026Article
- Quantum phase classification via partial tomography-based quantum hypothesis testing.Scientific reports · 2026Article
- ASAS-NANP SYMPOSIUM: MATHEMATICAL MODELING IN ANIMAL NUTRITION: quantum computing in agricultural sciences: from theory to reality.Journal of animal science · 2026Review
- Numerical study of macroscopic thermal diodes: Influence of interface topography and contact resistance.iScience · 2025Article
- Quantum machine learning driven optimization of nutrient-hormone interactions for enhanced in vitro regeneration of common bean.BMC plant biology · 2025Article
- Quantum Computing in the Next-Generation Computational Biology Landscape: From Protein Folding to Molecular Dynamics.Molecular biotechnology · 2024Review
- Review
- Biology and medicine in the landscape of quantum advantages.Journal of the Royal Society, Interface · 2022Review
- Taming numerical errors in simulations of continuous variable non-Gaussian state preparation.Scientific reports · 2022Article
- Article
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Abstract
Understanding quantum phase transitions in physical systems is fundamental to characterize their behavior at low temperatures. Achieving this requires both accessing good approximations to the ground state and identifying order parameters to distinguish different phases. Addressing these challenges, our work introduces a hybrid algorithm that combines quantum optimization with classical machine learning. This approach leverages the capability of near-term quantum computers to prepare locally trapped states through finite optimization. Specifically, we apply LASSO for identifying conventional phase transitions and the Transformer model for topological transitions, utilizing these with a sliding window scan of Hamiltonian parameters to learn appropriate order parameters and locate critical points. We validated the method with numerical simulations and real-hardware experiments on Rigetti's Ankaa 9Q-1 quantum computer. This protocol provides a framework for investigating quantum phase transitions with shallow circuits, offering enhanced efficiency and, in some settings, higher precision-thus contributing to the broader effort to integrate near-term quantum computing and machine learning.
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