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Quantum Information Processing

Quantum learning of concentrated Boolean functions

  • 01-07-2022
Published in:
Authors
Krishna Palem
Duc Hung Pham
M. V. Panduranga Rao
Published in
Quantum Information Processing | Issue 7/2022

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Abstract

The article delves into the intersection of quantum computing and machine learning, specifically focusing on the quantum learning of Boolean functions. It introduces the concept of concentration in Boolean functions and discusses how quantum algorithms can improve the learning process. The authors present three main results: an improved quantum learning algorithm, a lower bound on the number of quantum queries needed for distribution-independent learning, and a proof that exact learning requires exponentially more queries than PAC learning. The article also compares these results with classical algorithms and highlights the potential for quantum computing to significantly enhance machine learning processes.
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previous article Reality variation under monitoring with weak measurements
next article A mediated semi-quantum protocol for millionaire problem based on high-dimensional Bell states
Title
Quantum learning of concentrated Boolean functions
Authors
Krishna Palem
Duc Hung Pham
M. V. Panduranga Rao
Publication date
01-07-2022
Publisher
Springer US
Published in
Quantum Information Processing / Issue 7/2022
Print ISSN: 1570-0755
Electronic ISSN: 1573-1332
DOI
https://doi.org/10.1007/s11128-022-03607-5
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