# Parametric Probabilistic Quantum Memory

arXiv: https://arxiv.org/abs/2001.04798 · HTML: https://mlsanity.com/paper/2001.04798/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jan 2020.

## Built on by (most cited first, 2 of 2)

- [EP-PQM: Efficient Parametric Probabilistic Quantum Memory with Fewer Qubits and Gates](https://mlsanity.com/paper/2201.07265/) (2022)
- [Mirror modular cloning and fast quantum associative retrieval](https://mlsanity.com/paper/2206.01644/) (2022)

## Abstract (arXiv)

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.