Parametric Probabilistic Quantum Memory

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. Introduction Quantum Computing is a computational paradigm that has been harvesting increasing attention for decades now. Several quantum algorithms have time advantages over their best known classical counterparts [1, 2, 3, 4]. The current advances in quantum hardware are bringing us to the era of Noisy Intermediate-Scale Quantum (NISQ) computers [5]. The quest for quantum supremacy is the search for an efficient solution of a task in a quantum computer that current classical computers are not able to efficiently solve. Some authors argue that given the current state of the art, we will achieve quantum supremacy in the next few years [6]. One of the approaches to achieve this supremacy and to expand the potential applications of quantum computers is through quantum machine learning [7]. Machine learning (ML) [8] aims at developing automated ways for computers to learn a specific task from a given set of data samples.