Parametric Probabilistic Quantum Memory
This work addresses pattern classification for quantum computing applications, but it appears incremental as it builds on an existing PQM data structure.
The authors tackled the problem of pattern classification by proposing a parametric version of Probabilistic Quantum Memory (PQM) and a quantum circuit suitable for NISQ computers, achieving experimental verification on a 5-qubit quantum computer and classical evaluation on public benchmark datasets.
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.