[Power Up] Quantum Enhanced GAN for HEP

[TDHTTTT]

Team Name:

QC@UCI

Project Description:

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 multi-basis QCBM and CaloGAN

• Run experiments on the ECal Shower dataset [4] 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

Source code:

QC-UCI/QHack

Resource Estimate:

We plan to use Floq for quantum circuit simulations. The AWS budget will mainly be spent on training QC-AAN, both the classical GAN trianing and the quantum QCBM circuit training. A rough cost estimate:

• Training classical CaloGAN on K80: 10 (12 mins/epoch 50 epochs) * $1.125 per hour = $110.25
• Training QCBM (N qubit) with CaloGAN on Rigetti: 10 (10E4 shots/measurement basis 2^N 10 epochs) $3.5E-4

So, the cost of training QCBM dominates our budget and we probably need ~$1k to get meaningful results. Note if the cost of trianing QCBM gets too large, we might be able to do some tricks by sampling or freezing weight after certain epochs. Thanks again to Floq and AWS for those wonderful computing time!

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

[co9olguy]

Thanks so much for the draft submission @TDHTTTT! :+1:

[co9olguy]

Thanks for your Power Up Submission @TDHTTTT !

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