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Quantum Machine Learning

The Power of One Clean Qubit in Supervised Machine Learning

arXiv
Authors: Mahsa Karimi, Ali Javadi-Abhari, Christoph Simon, Roohollah Ghobadi

Year

2022

Paper ID

58308

Status

Preprint

Abstract Read

~2 min

Abstract Words

114

Citations

N/A

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.

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  • This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
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  • 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...

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References & Citation Signals

[1] DOI https://doi.org/arXiv:2210.09275 [2] arXiv https://arxiv.org/abs/2210.09275 [3] Publisher https://arxiv.org/abs/2210.09275

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