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About this paper
Robert Klassert, Andreas Baumbach, Mihai A. Petrovici, Martin Gärttner
The authors use an analog spiking-neuron chip to sample quantum ground states, encoding spins in neuron refractory states and using hidden neurons to model correlations. Chip-generated samples support energy-minimizing training for a ferromagnetic transverse-field Ising model. At h/J=1, fidelity exceeds 99% in the tested size study, but results establish a proof of principle only through ten spins.
Summary by alphaXiv.
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