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Occam's Quantum Razor: How Quantum Mechanics can reduce the complexity of classical models
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Mathematical models are an essential component of quantitative science. They generate predictions about the future, based on information available in the present. In the spirit of Occam's razor, simpler is better; should two models make identical predictions, the one that requires less input is preferred. Yet, for almost all stochastic processes, even the provably optimal classical models waste information. The amount of input information they demand exceeds the amount of predictive information they output. We systematically construct quantum models that break this classical bound, and show that the system of minimal entropy that simulates such processes must necessarily feature quantum dynamics. This indicates that many observed phenomena could be significantly simpler than classically possible should quantum effects be involved.
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Variational learning of integrated quantum photonic circuits
A variational learning method that treats an integrated photonic circuit as a single trainable optical matrix is demonstrated on a silicon chip for a CNOT gate and for quantum stochastic simulation.
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