Quantum adiabatic machine learning with zooming (QAML-Z) on D-Wave Advantage Quantum Computer

QAML-Z is a quantum machine learning (QML) algorithm that categorizes high energy physics (HEP) events amongst a same-particle, different-topology background. In theory, QML utilizes the unique nature of quantum computers to perform faster than classical machine learning. However, in practice it's difficult to prove this quantum advantage, let alone physically implement it on a quantum computer; due mostly to the current, error-ful quantum computing (QC) hardware. In this presentation, we examine QAML-Z's performance on newly-updated QC hardware, and discuss the possibility of implementing quantum adiabatic correction (QAC) on QAML-Z.