Machine learning of high dimensional data on a noisy quantum processor

TL;DR

This paper demonstrates the use of Google's Sycamore quantum processor to perform high-dimensional data classification directly on real spectral features, achieving competitive accuracy without data reduction.

Contribution

It introduces a quantum kernel method applied to high-dimensional, real-world data on a noisy quantum processor, surpassing previous low-dimensional experiments.

Findings

Successfully classified 67-dimensional data using 17 qubits.

Achieved accuracy comparable to noiseless simulation and classical methods.

Showed feasibility of high-dimensional quantum machine learning on real hardware.

Abstract

We present a quantum kernel method for high-dimensional data analysis using Google's universal quantum processor, Sycamore. This method is successfully applied to the cosmological benchmark of supernova classification using real spectral features with no dimensionality reduction and without vanishing kernel elements. Instead of using a synthetic dataset of low dimension or pre-processing the data with a classical machine learning algorithm to reduce the data dimension, this experiment demonstrates that machine learning with real, high dimensional data is possible using a quantum processor; but it requires careful attention to shot statistics and mean kernel element size when constructing a circuit ansatz. Our experiment utilizes 17 qubits to classify 67 dimensional data - significantly higher dimensionality than the largest prior quantum kernel experiments - resulting in classification accuracy that is competitive with noiseless simulation and comparable classical techniques.