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arXiv 2307.15840quant-phcs.LG
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基于中性原子的量子核估计用于监督分类：一种基于门的方法

Quantum Kernel Estimation With Neutral Atoms For Supervised Classification: A Gate-Based Approach

Politecnico di Torino（都灵理工大学）

Marco Russo, Edoardo Giusto, Bartolomeo Montrucchio

备注：
Version accepted for presentation at IEEE QCE23 conference
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AI总结：

本文提出了一种基于门模型的中性原子量子核估计方法，通过从激光脉冲推导门并构建特征映射来训练支持向量机，解决了超导设备连通性不足的问题，并验证了其在小数据集上的高准确率。

摘要：

量子核估计（QKE）是一种利用量子计算机来估计经典计算机难以计算的核函数的技术，随后经典计算机利用该核函数来训练支持向量机（SVM）。由于实现难以经典模拟的特征映射需要大量的2-局部算符，因此需要高量子位连通性，而这在目前的超导设备上无法实现。出于这个原因，可以使用中性原子量子计算机，因为它们允许更自由地排列原子。文献中可以找到基于中性原子的QKE的例子，但它们侧重于图学习并使用模拟方法。本文提出了一种基于门模型的通用方法。在从激光脉冲推导出1-量子位和2-量子位门之后，实现了在3个量子位上进行特征映射的参数化序列。然后使用该序列从数据集经验性地计算核矩阵，最终用于训练SVM。还展示了利用该技术允许的更灵活的原子排列，该过程可以推广到N个量子位。尽管数据集较小且分离度较低，但准确率被证明很高。这是第一篇不仅提出了显式推导通用门集算法的论文，还提出了使用门模型在中性原子设备上估计一般问题量子核方法的论文。

英文摘要：

Quantum Kernel Estimation (QKE) is a technique based on leveraging a quantum computer to estimate a kernel function that is classically difficult to calculate, which is then used by a classical computer for training a Support Vector Machine (SVM). Given the high number of 2-local operators necessary for realizing a feature mapping hard to simulate classically, a high qubit connectivity is needed, which is not currently possible on superconducting devices. For this reason, neutral atom quantum computers can be used, since they allow to arrange the atoms with more freedom. Examples of neutral-atom-based QKE can be found in the literature, but they are focused on graph learning and use the analogue approach. In this paper, a general method based on the gate model is presented. After deriving 1-qubit and 2-qubit gates starting from laser pulses, a parameterized sequence for feature mapping on 3 qubits is realized. This sequence is then used to empirically compute the kernel matrix starting from a dataset, which is finally used to train the SVM. It is also shown that this process can be generalized up to N qubits taking advantage of the more flexible arrangement of atoms that this technology allows. The accuracy is shown to be high despite the small dataset and the low separation. This is the first paper that not only proposes an algorithm for explicitly deriving a universal set of gates but also presents a method of estimating quantum kernels on neutral atom devices for general problems using the gate model.

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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