Parametric Probabilistic Quantum Memory

• URL: http://arxiv.org/abs/2001.04798v1
• Date: Sat, 11 Jan 2020 11:41:05 GMT
• Title: Parametric Probabilistic Quantum Memory
• Authors: Rodrigo S. Sousa, Priscila G.M. dos Santos, Tiago M.L. Veras, Wilson R. de Oliveira and Adenilton J. da Silva
• Abstract summary: Probabilistic Quantum Memory (PQM) is a data structure that computes the distance from a binary pattern stored in superposition on the memory. In this work, we propose an improved parametric version of the PQM to perform pattern classification. We also present a PQM quantum circuit suitable for Noisy Intermediate Scale Quantum (NISQ) computers.
• Score: 1.412197703754359
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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