2013/03/31 by Seokwon Yoo, Jeongho Bang, Changhyoup Lee +1 · 1 citation
Computer Science · Physics and Astronomy · #Algorithm #Boolean function #Computer science #Function (biology) #Neural Networks and Reservoir Computing #Parallel computing #Physics #Quantum #Quantum Computing Algorithms and Architecture #Quantum Information and Cryptography #Quantum algorithm #Quantum mechanics #Quantum superposition #Speedup #Superposition principle #Theoretical computer science #Unitary state #quant-ph
paper · pdf · doi:10.1088/1367-2630/16/10/103014
published as New J Phys, 2014 vol. 6 (10) 103014 · 15 pages, 5 figures, 3 tables
openalex publication_date 2014/10/09 · arxiv created 2014/10/14 · arxiv updated 2014/10/15 · openalex created_date 2016/06/24 · openalex updated_date 2026/08/05
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.