Dual-Parameterized Quantum Circuit GAN Model in High Energy Physics
| Autor: | Ross Duncan, Su Yeon Chang, Sofia Vallecorsa, Steven Herbert, Elías F. Combarro |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
Quantum Physics Computer Science - Machine Learning Discriminator Pixel Calorimeter (particle physics) Computer science Physics QC1-999 cs.LG Monte Carlo method FOS: Physical sciences Parameterized complexity Computing and Computers Machine Learning (cs.LG) Quantum circuit quant-ph Probability distribution Quantum Physics (quant-ph) Quantum Algorithm General Theoretical Physics |
| Zdroj: | EPJ Web of Conferences, Vol 251, p 03050 (2021) |
| Popis: | 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. 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|