Hybrid QSCI method with LCNot-UCCSD ansatz and RBM-based configuration recovery enables NISQ-era molecular simulations, demonstrated on small molecules and DMET-embedded protein-ligand systems.
Restricted boltzmann machine learning for solving strongly correlated quantum systems,
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Canonical mapping of quantum-dot-superconductor clusters enables neural quantum-state calculations that reveal trivial singlet, Heisenberg-like, and critical regimes with 1D gaplessness and 2D triplet states.
An analogy is drawn equating hidden units in Boltzmann machines with discrete Feynman paths, yielding quantum-circuit representations and links to inverse scattering for interpretability.
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Bridging the NISQ and Fault-Tolerant Regimes: Generative-ML-Assisted Quantum Selected CI for Molecular Simulations
Hybrid QSCI method with LCNot-UCCSD ansatz and RBM-based configuration recovery enables NISQ-era molecular simulations, demonstrated on small molecules and DMET-embedded protein-ligand systems.
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Correlated States in Quantum Dot Clusters Coupled to a Common Superconductor
Canonical mapping of quantum-dot-superconductor clusters enables neural quantum-state calculations that reveal trivial singlet, Heisenberg-like, and critical regimes with 1D gaplessness and 2D triplet states.