Quantum Enhanced GAN for HEP

Overview

To enhance the Generative Adversarial Networks (GAN) used in the High Energy Physics (HEP) community for fast event simulation with Quantum Circuit Born Machine (QCBM), a versatile and efficient quantum generative model, to sample the prior (latent space). The quantum enhanced architecture, Quantum Circuit Associative Adversarial Network (QC-AAN), was shown previously to not only have similar performance as DCGAN but also have practical quantum advantages such as greater training stability on MNIST [1]. Instabilities of the training caused by diverging gradient and vanishing gradient are a major practical concern, especially for the HEP community*[2]. So, if a QC-AAN can make the training for GANs more robust, we would expect it to have practical value for the HEP community. We plan to build upon CaloGAN [3], a popular architecture to generate HEP detector responses and use vanilla CaloGAN as a baseline for comparison.

* To overcome the training instability, HEP community often uses Wasserstein GANs. Due to time constraints, we plan to investigate a quantum enhanced Wassertein GANs in the future.

Procedure

• Construct QC-AAN with QCBM and CaloGAN

• Run experiments on partial ECal Shower dataset [4] and compare QC-ANN against vanilla CaloGAN with the metrics in the next section

• Optimize multi-basis QCBM

• Run experiments on full ECal Shower dataset and compare QC-ANN against vanilla CaloGAN with the metrics in the next section

• If time permits, repeat the experiments and compare it against

  • Wasserstein CaloGAN
  • Restricted Boltzmann Machine (RBM) based AAN

Metrics

• Inception score
• HEP based similarity score
  • 1-D showering statistics
  • Energy flow polynomials (EFPs)
• Training stability
• Mode (energy) diversity

Presentation

For a non-techinical overview, please refer to this slides.

Reference

[1] M. S. Rudolph, N. B. Toussaint, A. Katabarwa, S. Johri, B. Peropadre, and A. Perdomo-Ortiz, Generation of High-Resolution Handwritten Digits with an Ion-Trap Quantum Computer, (2020).

[2] A. Butter and T. Plehn, Generative Networks for LHC Events, ArXiv:2008.08558 [Hep-Ph] (2020).

[3] M. Paganini, L. de Oliveira, and B. Nachman, CaloGAN: Simulating 3D High Energy Particle Showers in Multi-Layer Electromagnetic Calorimeters with Generative Adversarial Networks, Phys. Rev. D 97, 014021 (2018).

[4] Nachman, Benjamin; de Oliveira, Luke; Paganini, Michela (2017), “Electromagnetic Calorimeter Shower Images”, Mendeley Data, V1, doi: 10.17632/pvn3xc3wy5.1