A new neural quantum state ansatz for bosons in the grand canonical ensemble achieves competitive variational energies in 1D and 2D systems and provides access to one-body reduced density matrices.
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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.
Indirect measurements in quantum reservoir computing improve execution time scaling, overall performance, and memory capacity over projective measurements and classical feedback methods.
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Neural network quantum states in the grand canonical ensemble
A new neural quantum state ansatz for bosons in the grand canonical ensemble achieves competitive variational energies in 1D and 2D systems and provides access to one-body reduced density matrices.
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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.
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Harnessing quantum back-action for time-series processing
Indirect measurements in quantum reservoir computing improve execution time scaling, overall performance, and memory capacity over projective measurements and classical feedback methods.