Directly training soft-unitary matrices with a unitarity regularization term and converting them to circuits via alignment enables faster training and lower loss than gate-based optimization on small quantum classification and reinforcement learning tasks.
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Brillouin-Wigner perturbation theory plus Hartree-Fock mean-field approximation upgrades quasiparticle nuclear Hamiltonians, yielding <0.2% and ~2% ground-state energy errors versus exact shell-model results in the sd shell while preserving qubit efficiency.
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Soft-Quantum Algorithms
Directly training soft-unitary matrices with a unitarity regularization term and converting them to circuits via alignment enables faster training and lower loss than gate-based optimization on small quantum classification and reinforcement learning tasks.
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Improved quasiparticle nuclear Hamiltonians for quantum computing
Brillouin-Wigner perturbation theory plus Hartree-Fock mean-field approximation upgrades quasiparticle nuclear Hamiltonians, yielding <0.2% and ~2% ground-state energy errors versus exact shell-model results in the sd shell while preserving qubit efficiency.