Mathematical solutions to thorny quantum problems can be found more quickly by exploiting the correspondence between the statistical methods used in deep learning and techniques for implementing quantum simulations, a team led by a RIKEN researcher has shown in a new study published in the Journal of High Energy Physics.
Mathematical solutions to thorny quantum problems can be found more quickly by exploiting the correspondence between the statistical methods used in deep learning and techniques for implementing quantum simulations, a team led by a RIKEN researcher has shown in a new study published in the Journal of High Energy Physics.
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