A quantum speedup in machine learning: Finding a N-bit Boolean function for a classification

TL;DR

This paper demonstrates that quantum machines can learn N-bit Boolean functions faster than classical machines by leveraging quantum superposition, expanding the solution space, as shown through numerical simulations.

Contribution

It provides a comparative analysis showing quantum coherence enhances learning speed and solution regions in Boolean function classification.

Findings

Quantum machines outperform classical ones in learning speed.

Quantum superposition expands the acceptable solution regions.

Numerical simulations confirm the quantum advantage.

Abstract

We compare quantum and classical machines designed for learning an N-bit Boolean function in order to address how a quantum system improves the machine learning behavior. The machines of the two types consist of the same number of operations and control parameters, but only the quantum machines utilize the quantum coherence naturally induced by unitary operators. We show that quantum superposition enables quantum learning that is faster than classical learning by expanding the approximate solution regions, i.e., the acceptable regions. This is also demonstrated by means of numerical simulations with a standard feedback model, namely random search, and a practical model, namely differential evolution.