论文标题
iqgan：在NISQ设备上用于图像合成的稳健量子生成对抗网络
IQGAN: Robust Quantum Generative Adversarial Network for Image Synthesis On NISQ Devices
论文作者
论文摘要
在这项工作中，我们提出了iqgan，这是一个用于多标志综合的量子生成对抗网络（GAN）框架，该框架可以在嘈杂的中间尺度量子（NISQ）设备上有效实现。我们在初步研究中研究了当前量子gan的劣等生成性能的原因，并得出结论，可调节的输入编码器是确保高质量数据合成的关键。然后，我们提出了具有可训练的多Quantum量子编码器的IQGAN架构，该编码器将经典数据有效地嵌入量子状态。此外，我们提出了一个紧凑型量子发生器，可显着降低NISQ设备上的设计成本和电路深度。 IBM量子处理器和量子模拟器的实验结果表明，在对生成的样品，模型收敛和量子计算成本的定性和定量评估中，IQGAN优于最先进的量子gans。
In this work, we propose IQGAN, a quantum Generative Adversarial Network (GAN) framework for multiqubit image synthesis that can be efficiently implemented on Noisy Intermediate Scale Quantum (NISQ) devices. We investigate the reasons for the inferior generative performance of current quantum GANs in our preliminary study and conclude that an adjustable input encoder is the key to ensuring high-quality data synthesis. We then propose the IQGAN architecture featuring a trainable multiqubit quantum encoder that effectively embeds classical data into quantum states. Furthermore, we propose a compact quantum generator that significantly reduces the design cost and circuit depth on NISQ devices. Experimental results on both IBM quantum processors and quantum simulators demonstrated that IQGAN outperforms state-of-the-art quantum GANs in qualitative and quantitative evaluation of the generated samples, model convergence, and quantum computing cost.
