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arxiv: 1604.07517 · v3 · pith:7NV6YWNBnew · submitted 2016-04-26 · 🪐 quant-ph

Quantifiable simulation of quantum computation beyond stochastic ensemble computation

classification 🪐 quant-ph
keywords inequalitysenmcomputationensemblemachinemodelhypothesisprobabilistic
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In this study, a distinctive feature of quantum computation (QC) is characterized. To this end, a seemingly-powerful classical computing model, called "stochastic ensemble machine (SEnM)," is considered. The SEnM runs with an ensemble consisting of finite copies of a single probabilistic machine, hence is as powerful as a probabilistic Turing machine (PTM). Then the hypothesis--that is, the SEnM can effectively simulate a general circuit model of QC--is tested by introducing an information-theoretic inequality, named readout inequality. The inequality is satisfied by the SEnM and imposes a critical condition: if the hypothesis holds, the inequality should be satisfied by the probing model of QC. However, it is shown that the above hypothesis is not generally accepted with the inequality violation, namely, such a simulation necessarily fails, implying that PTM $\subseteq$ QC.

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