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

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.

15 pages, 5 figures, 3 tables

References in corpus (5)

• Quantum support vector machine for big data classification
• Quantum algorithms for supervised and unsupervised machine learning
• Improved extremal optimization for the Ising spin glass
• Strategy for quantum algorithm design assisted by machine learning
• Procedures for realizing an approximate universal NOT gate

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