The Power of One Clean Qubit in Supervised Machine Learning

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.

9 pages, 11 figures

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• Kernel methods in machine learning
• Quantum discord and the power of one qubit
• Necessary and sufficient condition for non-zero quantum discord
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• The Power of One Clean Qubit in Supervised Machine Learning

Cited by in corpus (6)

• Tsallis relative $\alpha$ entropy of coherence dynamics in Grover's search algorithm
• The Power of One Clean Qubit in Supervised Machine Learning
• Frozen condition of quantum coherence
• Expressivity of deterministic quantum computation with one qubit
• Single-Qudit Quantum Neural Networks for Multiclass Classification
• State convertibility under genuinely incoherent operations