# Quenched Quantum Feature Maps

## TL;DR

This paper introduces a quantum feature mapping method using quench dynamics in quantum spin glasses, significantly improving machine learning performance on complex datasets and demonstrating quantum advantage in real-world applications.

## Contribution

It presents a novel quantum feature map leveraging nonadiabatic evolution in spin glasses, enhancing classical ML models and achieving quantum advantage in practical tasks.

## Key findings

- ML models benefit most from fast coherent quantum regimes
- Quantum feature maps improve classical model performance by up to 210%
- Demonstrated quantum advantage on high-dimensional datasets in real-world applications

## Abstract

We propose a quantum feature mapping technique that leverages the quench dynamics of a quantum spin glass to extract complex data patterns at the quantum-advantage level for academic and industrial applications. We demonstrate that encoding a dataset information into disordered quantum many-body spin-glass problems, followed by a nonadiabatic evolution and feature extraction via measurements of expectation values, significantly enhances machine learning (ML) models. By analyzing the performance of our protocol over a range of evolution times, we empirically show that ML models benefit most from feature representations obtained in the fast coherent regime of a quantum annealer, particularly near the critical point of the quantum dynamics. We demonstrate the generalization of our technique by benchmarking on multiple high-dimensional datasets, involving over a hundred features, in applications including drug discovery and medical diagnostics. Moreover, we compare against a comprehensive suite of state-of-the-art classical ML models and show that our quantum feature maps can enhance the performance metrics of the baseline classical models up to 210%. Our work presents the first quantum ML demonstrations at the quantum-advantage level, bridging the gap between quantum supremacy and useful real-world academic and industrial applications.