可扩展的张量网络模拟用于量子-经典双核
Scalable Tensor Network Simulation for Quantum-Classical Dual Kernel
AI总结：
本文提出了一种可扩展的张量网络框架，用于量子-经典双核的模拟，通过比较不同核在不同特征维度下的性能，证明了该双核在提升分类性能和缓解大规模退化方面的有效性。
摘要：
本文提出了一种高效且可扩展的张量网络框架，用于量子核电路模拟，以减轻随着量子比特数量和数据规模增加而产生的实际成本。该框架能够系统地评估线性混合的量子-经典双核，最多支持784个量子比特。使用Fashion-MNIST数据集，在特征维度从2到784之间，将经典核、量子核和量子-经典双核的测试数据集分类性能进行比较，其中编码特征与量子比特之间有一一映射。我们的结果表明，量子-经典双核在所有情况下均优于单一核基线，随着维度增加保持稳定，并缓解了量子核在大规模下的退化问题。对学习到的混合权重的分析表明，在低于128个特征时，量子贡献占主导地位，而超过128个特征时，经典贡献变得越来越重要，这表明经典核在对抗集中效应和硬件噪声方面提供了稳定的基础，同时在低维度下保留了量子收益。
英文摘要：
This paper presents an efficient and scalable tensor network framework for quantum kernel circuit simulation, alleviating practical costs associated with increasing qubit counts and data size. The framework enables systematic large-scale evaluation of a linearly mixed quantum-classical dual kernel of up to 784 qubits. Using Fashion-MNIST, the classification performance of the test dataset is compared between a classical kernel, a quantum kernel, and the quantum-classical dual kernel across the feature dimensions from 2 to 784, with a one-to-one mapping between encoded features and qubits. Our result shows that the quantum-classical dual kernel consistently outperforms both single-kernel baselines, remains stable as the dimensionality increases, and mitigates the large-scale degradation observed in the quantum kernel. Analysis of the learned mixing weights indicates that quantum contributions dominate below 128 features, while classical contributions become increasingly important beyond 128, suggesting that the classical kernel provides a stabilizing anchor against concentration effects and hardware noise while preserving quantum gains at lower dimensions.
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。
