mlsanityData to 7 Oct 2026

Dual-Parameterized Quantum Circuit GAN Model in High Energy Physics

arXiv 2103.15470 ↗First posted 29 Mar 2021quant-ph cs.LGcited by 9

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Abstract (arXiv)

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.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 5 of its 21 references that are in the map. First posted on arXiv 29 Mar 2021.

Builds on

5 of 21 references.

References · 5

  1. 2015Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networkscited by 2,048
  2. 2016Conditional Image Synthesis With Auxiliary Classifier GANscited by 562
  3. 2019Low-depth gradient measurements can improve convergence in variational hybrid quantum-classical algorithmscited by 31
  4. 2020Noise robustness and experimental demonstration of a quantum generative adversarial network for continuous distributionscited by 11
  5. 2019Efficient Online Quantum Generative Adversarial Learning Algorithms with Applicationscited by 6

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 3; 2022: 2; 2023: 1; 2024: 1; 2025: 1; 2026: 1.

9 papers.

  1. 2023Machine Learning for Anomaly Detection in Particle Physicscited by 29
  2. 2021Hybrid Quantum Classical Graph Neural Networks for Particle Track Reconstructioncited by 20
  3. 2021Style-based quantum generative adversarial networks for Monte Carlo eventscited by 19
  4. 2021Quantum Quantile Mechanics: Solving Stochastic Differential Equations for Generating Time-Seriescited by 10
  5. 2025Tensor Network for Anomaly Detection in the Latent Space of Proton Collision Events at the LHCcited by 5
  6. 2022Conditional Born machine for Monte Carlo event generationcited by 4
  7. 2024Guided Quantum Compression for High Dimensional Data Classificationcited by 3
  8. 2022Running the Dual-PQC GAN on noisy simulators and real quantum hardwarecited by 2
  9. 2026Correcting Sensor-Induced Distribution Drift with Wasserstein Adversarial Learningcited by 0