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arXiv 2408.04709quant-ph
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中继器节点的量子神经网络训练

Quantum Neural Network Training of a Repeater Node

Diego Fuentealba, Jack Dahn, James Steck, Elizabeth Behrman

发表信息：
Information 2025, 16(7)
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AI总结：

针对量子中继器节点交换门噪声容忍度差的问题，构建量子神经网络执行交换操作，发现其无需额外训练即可扩展且噪声容忍度随量子比特数增加而提升。

摘要：

构建鲁棒且可扩展的量子门是量子计算领域中一个独特的难题。现实世界的量子计算机受到多种形式的噪声影响，这些噪声由量子电路的退相干和弛豫时间表征，使得构建高效的量子算法非常困难。一个例子是量子中继器节点，这是一种交换两个纠缠输入和输出量子比特状态的电路。鲁棒的量子中继器是长距离量子网络的必要构建模块。针对这个问题存在一个解决方案，称为交换门，但其噪声容忍度较差。机器学习可能是实现高效和鲁棒量子算法设计的关键，正如其在学习控制其他噪声和高度非线性系统方面所展现的能力所证明的那样。在这里，构建了一个量子神经网络（QNN）来执行交换操作，并将训练后的QNN解决方案与标准交换门进行比较。量子比特和QNN系统在MATLAB中构建，并在理想条件下进行训练，然后人为地向系统添加噪声以测试鲁棒性。我们发现QNN很容易泛化到两个量子比特，并且可以在无需额外训练的情况下扩展到更多量子比特。我们还发现，随着量子比特数量的增加，噪声容忍度也随之增加，这意味着足够大的系统可以产生极具噪声容忍度的结果。这开始探索神经网络构建这些鲁棒系统的能力。

英文摘要：

The construction of robust and scalable quantum gates is a uniquely hard problem in the field of quantum computing. Real-world quantum computers suffer from many forms of noise, characterized by the decoherence and relaxation times of a quantum circuit, which make it very hard to construct efficient quantum algorithms. One example is a quantum repeater node, a circuit that swaps the states of two entangled input and output qubits. Robust quantum repeaters are a necessary building block of long-distance quantum networks. A solution exists for this problem, known as a swap gate, but its noise tolerance is poor. Machine learning may hold the key to efficient and robust quantum algorithm design, as demonstrated by its ability to learn to control other noisy and highly nonlinear systems. Here, a quantum neural network (QNN) is constructed to perform the swap operation and compare a trained QNN solution to the standard swap gate. The system of qubits and QNN is constructed in MATLAB and trained under ideal conditions before noise is artificially added to the system to test robustness. We find that the QNN easily generalizes for two qubits and can be scaled up to more qubits without additional training. We also find that as the number of qubits increases, the noise tolerance increases with it, meaning a sufficiently large system can produce extremely noise-tolerant results. This begins to explore the ability of neural networks to construct those robust systems.

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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