# Quantum Associative Memory in HEP Track Pattern Recognition

## TL;DR

This paper explores the potential of Quantum Associative Memory (QuAM) for real-time pattern recognition in high-energy physics experiments, assessing its practical limits on current quantum hardware and its advantages over classical algorithms.

## Contribution

It presents a software prototype of QuAM, analyzes topological limitations on IBM quantum processors, and evaluates its suitability for LHC data triggering tasks.

## Key findings

- QuAM shows promise for high-speed pattern recognition in HEP data
- Topological constraints limit the size of implementable QuAM instances on current hardware
- Quantum algorithms could reduce complexity in future HEP data processing

## Abstract

We have entered the Noisy Intermediate-Scale Quantum Era. A plethora of quantum processor prototypes allow evaluation of potential of the Quantum Computing paradigm in applications to pressing computational problems of the future. Growing data input rates and detector resolution foreseen in High-Energy LHC (2030s) experiments expose the often high time and/or space complexity of classical algorithms. Quantum algorithms can potentially become the lower-complexity alternatives in such cases. In this work we discuss the potential of Quantum Associative Memory (QuAM) in the context of LHC data triggering. We examine the practical limits of storage capacity, as well as store and recall errorless efficiency, from the viewpoints of the state-of-the-art IBM quantum processors and LHC real-time charged track pattern recognition requirements. We present a software prototype implementation of the QuAM protocols and analyze the topological limitations for porting the simplest QuAM instances to the public IBM 5Q and 14Q cloud-based superconducting chips.