量子噪声对量子生成对抗网络训练的影响
Impact of quantum noise on the training of quantum Generative Adversarial Networks
摘要：
当前有噪声的中等规模量子器件遭受各种固有量子噪声源。克服噪声的影响是一个主要的挑战，为此已经提出了不同的错误减轻和错误校正技术。在本文中，我们首次研究了量子生成对抗网络（qGAN）在不同类型量子噪声存在下的性能，重点是高能物理中的简化用例。特别是，我们探讨了读出和两个量子比特门错误对qGAN训练过程的影响。用IBM的Qiskit框架模拟一个有噪声的量子设备，我们研究了一个可靠的训练可能达到的错误率阈值。此外，我们还研究了在存在不同错误率的情况下，各种超参数对训练过程的重要性，并探讨了减轻读出错误对结果的影响。
英文摘要：
Current noisy intermediate-scale quantum devices suffer from various sources of intrinsic quantum noise. Overcoming the effects of noise is a major challenge, for which different error mitigation and error correction techniques have been proposed. In this paper, we conduct a first study of the performance of quantum Generative Adversarial Networks (qGANs) in the presence of different types of quantum noise, focusing on a simplified use case in high-energy physics. In particular, we explore the effects of readout and two-qubit gate errors on the qGAN training process. Simulating a noisy quantum device classically with IBM's Qiskit framework, we examine the threshold of error rates up to which a reliable training is possible. In addition, we investigate the importance of various hyperparameters for the training process in the presence of different error rates, and we explore the impact of readout error mitigation on the results.
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