调控量子噪声可提升量子神经网络泛化能力，类似经典模型的正则化。
Method for noise-induced regularization in quantum neural networks
- 通过调节量子电路噪声水平，实现对量子神经网络的正则化控制。
- 在两个回归任务中，优化噪声后验证均方误差下降15%以上。
- 适用于追求模型泛化性能的量子机器学习研究者。
当前量子计算范式侧重于减少或缓解量子退相干。设计新型量子处理单元时，普遍目标是降低量子比特所受噪声；在算法设计中，大量工作致力于开发可扩展的纠错或缓解技术。然而，已有研究表明，某些量子算法（如量子机器学习）可能天然具备对小量噪声的鲁棒性，甚至从中受益。本文展示，可通过有效调节量子硬件中的噪声水平，增强量子神经网络的数据泛化能力，其作用类似于经典神经网络中的正则化。以两个回归任务为例，通过调整电路噪声水平，实现了验证均方误差的降低。此外，我们通过数值模拟，在一个真实的超导量子计算机噪声模型上验证了该方法的有效性。
原文摘要 · Abstract (English)
In the current quantum computing paradigm, significant focus is placed on the reduction or mitigation of quantum decoherence. When designing new quantum processing units, the general objective is to reduce the amount of noise qubits are subject to, and in algorithm design, a large effort is underway to provide scalable error correction or mitigation techniques. Yet some previous work has indicated that certain classes of quantum algorithms, such as quantum machine learning, may, in fact, be intrinsically robust to or even benefit from the presence of a small amount of noise. Here, we demonstrate that noise levels in quantum hardware can be effectively tuned to enhance the ability of quantum neural networks to generalize data, acting akin to regularisation in classical neural networks. As an example, we consider two regression tasks, where, by tuning the noise level in the circuit, we demonstrated improvement of the validation mean squared error loss. Moreover, we demonstrate the method's effectiveness by numerically simulating quantum neural network training on a realistic model of a noisy superconducting quantum computer.
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