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arXiv 2001.047982020-01-11quant-phcs.LGstat.ML

Parametric Probabilistic Quantum Memory

Universidade Federal Rural de Pernambuco（伯南布哥联邦农村大学）；Universidade Federal de Pernambuco（伯南布哥联邦大学）

Rodrigo S. Sousa, Priscila G. M. dos Santos, Tiago M. L. Veras, Wilson R. de Oliveira, Adenilton J. da Silva

发表信息：: Neurocomputing 416 (2020): 360-369
DOI：: 10.1016/j.neucom.2020.01.116

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Probabilistic Quantum Memory (PQM) is a data structure that computes the distance from a binary input to all binary patterns stored in superposition on the memory. This data structure allows the development of heuristics to speed up artificial neural networks architecture selection. In this work, we propose an improved parametric version of the PQM to perform pattern classification, and we also present a PQM quantum circuit suitable for Noisy Intermediate Scale Quantum (NISQ) computers. We present a classical evaluation of a parametric PQM network classifier on public benchmark datasets. We also perform experiments to verify the viability of PQM on a 5-qubit quantum computer.

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