Generative models, and Generative Adversarial Networks (GAN) in particular, are being studied as possible alternatives to Monte Carlo simulations. It has been proposed that, in certain circumstances, simulation using GANs can be sped-up by using quantum GANs (qGANs). We present a new design of qGAN, the dual-Parameterized Quantum Circuit(PQC) GAN, which consists of a classical discriminator and two quantum generators which take the form of PQCs. The first PQC learns a probability distribution over N-pixel images, while the second generates normalized pixel intensities of an individual image for each PQC input. With a view to HEP applications, we evaluated the dual-PQC architecture on the task of imitating calorimeter outputs, translated into pixelated images. The results demonstrate that the model can reproduce a fixed number of images with a reduced size as well as their probability distribution and we anticipate it should allow us to scale up to real calorimeter outputs.
翻译:作为蒙特卡洛模拟的可能的替代方法,目前正在研究产生模型,特别是基因反转网络(GAN),建议在某些情况下,使用GANs的模拟可以通过量子GANs(qGANs)加速。我们展示了QGAN的新设计,即由古典歧视器和两种量子生成器组成的双量子电路(PQC)GAN。第一个PQC学习了N-像素图像的概率分布,而第二个PQC的图像生成了每个PQC输入的单个图像的正常像素强度。为了应用HEP,我们评估了模拟焦量计输出的双重PQC结构,将其转换成像素图像。结果显示,模型可以复制一定数量的图像,其尺寸和概率分布都较小,我们预计它应该使我们能够将图像的尺寸提高到真实卡略数输出量。