A perceptrain-based variational ansatz achieves relative ground-state energy accuracy of 10^{-5} (VMC) to 10^{-6} (GFMC) on a 10x10 transverse-field Ising model with 1/r^6 interactions using ranks of only 2-5.
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Neural-network quantum states are used to compute spectra of fully-heavy multiquarks in a non-relativistic quark model, claiming to overcome dimensionality issues with superior accuracy over prior approximations.
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Hybrid between biologically and quantum-inspired many-body states
A perceptrain-based variational ansatz achieves relative ground-state energy accuracy of 10^{-5} (VMC) to 10^{-6} (GFMC) on a 10x10 transverse-field Ising model with 1/r^6 interactions using ranks of only 2-5.
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Fully-heavy multiquarks in neural-network quantum states
Neural-network quantum states are used to compute spectra of fully-heavy multiquarks in a non-relativistic quark model, claiming to overcome dimensionality issues with superior accuracy over prior approximations.