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Occam's Quantum Razor: How Quantum Mechanics can reduce the complexity of classical models

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arxiv 1102.1994 v5 pith:MRZLQH6X submitted 2011-02-09 quant-ph cond-mat.stat-mech

classification quant-phcond-mat.stat-mech
keywords modelsquantuminformationclassicaltheyamountinputoccam
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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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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Variational learning of integrated quantum photonic circuits

    quant-ph 2024-11 conditional novelty 6.0 of 10

    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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