Dual-Parameterized Quantum Circuit GAN Model in High Energy Physics
We are pleased to share details of a scientific paper, posted on the pre-print respository arxiv carried out in collaboration with CERN. The authors propose the dual-PQC GAN for generative modelling applications in High-Energy Physics (specifically for generating typical calorimeter images). The major innovation of the project was using two PQCs to enable the generation of samples from an ensemble of typical images — something not possible with the conventional qGAN architecture. For this reason we expect the dual-PQC GAN to find application in image processing tasks more generally.
Su Yeon Chang, Steven Herbert, Sofia Vallecorsa, Elías F. Combarro, and Ross Duncan
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