Dual-Parameterized Quantum Circuit GAN Model in High Energy Physics
This work addresses simulation speed for high-energy physics researchers, but it is incremental as it builds on existing quantum GAN concepts with a new dual-circuit design.
The paper tackled the problem of speeding up simulations in high-energy physics by proposing a dual-parameterized quantum circuit GAN model to imitate calorimeter outputs as pixelated images, achieving reproduction of a fixed number of images with reduced size and their probability distribution.
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.