Predicting human-generated bitstreams using classical and quantum models

• URL: http://arxiv.org/abs/2004.04671v1
• Date: Thu, 9 Apr 2020 16:59:49 GMT
• Title: Predicting human-generated bitstreams using classical and quantum models
• Authors: Alex Bocharov, Michael Freedman, Eshan Kemp, Martin Roetteler, and Krysta M.Svore
• Abstract summary: School of thought contends that human decision making exhibits quantum-like logic. We emulate binary decision-making using low width, low depth, parameterized quantum circuits.
• Score: 3.009407292418984
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A school of thought contends that human decision making exhibits quantum-like logic. While it is not known whether the brain may indeed be driven by actual quantum mechanisms, some researchers suggest that the decision logic is phenomenologically non-classical. This paper develops and implements an empirical framework to explore this view. We emulate binary decision-making using low width, low depth, parameterized quantum circuits. Here, entanglement serves as a resource for pattern analysis in the context of a simple bit-prediction game. We evaluate a hybrid quantum-assisted machine learning strategy where quantum processing is used to detect correlations in the bitstreams while parameter updates and class inference are performed by classical post-processing of measurement results. Simulation results indicate that a family of two-qubit variational circuits is sufficient to achieve the same bit-prediction accuracy as the best traditional classical solution such as neural nets or logistic autoregression. Thus, short of establishing a provable "quantum advantage" in this simple scenario, we give evidence that the classical predictability analysis of a human-generated bitstream can be achieved by small quantum models.

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