论文标题
一个干净的量子在监督机器学习中的力量
The Power of One Clean Qubit in Supervised Machine Learning
论文作者
论文摘要
本文探讨了在监督机器学习中，量子相干性和量子不和谐在称为确定性量子计算（DQC1）的确定性量子计算模型中的潜在优势。我们表明，可以利用DQC1模型来开发一种有效的方法来估计复杂的内核函数。我们证明了连贯消耗与内核函数之间的简单关系，这是机器学习中的关键元素。本文使用DQC1模型在IBM硬件上介绍了二进制分类问题的实现，并分析了量子相干性和硬件噪声的影响。我们的提案的优点在于它利用量子和不和谐，这比纠缠更适合噪声。
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.
