  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.
