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arXiv 2210.09275quant-ph
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单清洁量子比特在监督机器学习中的威力

The Power of One Clean Qubit in Supervised Machine Learning

Mahsa Karimi, Ali Javadi-Abhari, Christoph Simon, Roohollah Ghobadi

AI总结:

本文研究DQC1模型在监督机器学习中的应用,提出利用量子失谐高效估计核函数,并在IBM硬件上实现二分类,展示了其对噪声的鲁棒性优势。

摘要:

本文探讨了量子相干性和量子失谐在非通用量子计算模型——单量子比特确定性量子计算(DQC1)中,于监督机器学习领域的潜在优势。我们证明,DQC1模型可被用于开发一种高效估计复杂核函数的方法。我们展示了相干性消耗与核函数(机器学习中的关键要素)之间的简单关系。本文在IBM硬件上使用DQC1模型实现了一个二分类问题,并分析了量子相干性和硬件噪声的影响。我们方案的优点在于利用了量子失谐,它比纠缠对噪声更具鲁棒性。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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