Parametric Probabilistic Quantum Memory
arXiv:2001.04798 · doi:10.1016/j.neucom.2020.01.116
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.
References in corpus (15)
- Quantum Computing in the NISQ era and beyond
- Quantum Machine Learning
- Quantum Mechanics helps in searching for a needle in a haystack
- Quantum algorithm for solving linear systems of equations
- Supervised learning with quantum enhanced feature spaces
- Quantum support vector machine for big data classification
- Quantum Computational Supremacy
- Quantum-enhanced machine learning
- Quantum generalisation of feedforward neural networks
- Implementing a distance-based classifier with a quantum interference circuit
- Probabilistic Quantum Memories
- Quantum perceptron over a field and neural network architecture selection in a quantum computer
- Quantum associative memory with linear and non-linear algorithms for the diagnosis of some tropical diseases
- Comment on "Probabilistic Quantum Memories"
- Quantum enhanced cross-validation for near-optimal neural networks architecture selection