  The Helstrom measurement (HM) is known to be the optimal strategy for
distinguishing non-orthogonal quantum states with minimum error. Previously, a
binary classifier based on classical simulation of the HM has been proposed. It
was observed that using multiple copies of the sample data reduced the
classification error. Nevertheless, the exponential growth in simulation
runtime hindered a comprehensive investigation of the relationship between the
number of copies and classification performance. We present an efficient
simulation method for an arbitrary number of copies by utilizing the
relationship between HM and state fidelity. Our method reveals that the
classification performance does not improve monotonically with the number of
data copies. Instead, it needs to be treated as a hyperparameter subject to
optimization, achievable only through the method proposed in this work. We
present a Quantum-Inspired Machine Learning binary classifier with excellent
performance, providing such empirical evidence by benchmarking on eight
datasets and comparing it with 13 hyperparameter optimized standard
classifiers.
