The Power of One Clean Qubit in Supervised Machine Learning

TL;DR

This paper investigates how the DQC1 quantum computing model can enhance supervised machine learning by efficiently estimating kernel functions, leveraging quantum coherence and discord, and demonstrates its implementation on IBM hardware.

Contribution

It introduces a novel method using DQC1 for kernel estimation in machine learning, emphasizing quantum discord's noise resilience and practical implementation.

Findings

DQC1 can efficiently estimate complex kernel functions.

Quantum discord offers robustness against hardware noise.

Successful implementation on IBM hardware demonstrates feasibility.

Abstract

This paper explores the potential benefits of quantum coherence and quantum discord in the non-universal quantum computing model called deterministic quantum computing with one qubit (DQC1) in supervised machine learning. We show that the DQC1 model can be leveraged to develop an efficient method for estimating complex kernel functions. We demonstrate a simple relationship between coherence consumption and the kernel function, a crucial element in machine learning. The paper presents an implementation of a binary classification problem on IBM hardware using the DQC1 model and analyzes the impact of quantum coherence and hardware noise. The advantage of our proposal lies in its utilization of quantum discord, which is more resilient to noise than entanglement.