mlsanityData to 14 Oct 2026

CaloQVAE : Simulating high-energy particle-calorimeter interactions using hybrid quantum-classical generative models

arXiv 2312.03179 ↗First posted 5 Dec 2023hep-ex cs.LG quant-phcited by 11

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Abstract (arXiv)

The Large Hadron Collider's high luminosity era presents major computational challenges in the analysis of collision events. Large amounts of Monte Carlo (MC) simulation will be required to constrain the statistical uncertainties of the simulated datasets below these of the experimental data. Modelling of high-energy particles propagating through the calorimeter section of the detector is the most computationally intensive MC simulation task. We introduce a technique combining recent advancements in generative models and quantum annealing for fast and efficient simulation of high-energy particle-calorimeter interactions.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 8 of its 32 references that are in the map. First posted on arXiv 5 Dec 2023.

Builds on

8 of 32 references.

References · 8

  1. 2016Discrete Variational Autoencoderscited by 125
  2. 2015Application of Quantum Annealing to Training of Deep Neural Networkscited by 37
  3. 2018DVAE++: Discrete Variational Autoencoders with Overlapping Transformationscited by 32
  4. 2023Ultra-High-Resolution Detector Simulation with Intra-Event Aware GAN and Self-Supervised Relational Reasoningcited by 25
  5. 2019A Path Towards Quantum Advantage in Training Deep Generative Models with Quantum Annealerscited by 15
  6. 2018GumBolt: Extending Gumbel trick to Boltzmann priorscited by 9
  7. 2022CaloDVAE : Discrete Variational Autoencoders for Fast Calorimeter Shower Simulationcited by 6
  8. 2020Training a quantum annealing based restricted Boltzmann machine on cybersecurity datacited by 5

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 4; 2025: 1; 2026: 5.

11 papers.

  1. 2023Deep Generative Models for Detector Signature Simulation: A Taxonomic Reviewcited by 18
  2. 2024Convolutional L2LFlows: Generating Accurate Showers in Highly Granular Calorimeters Using Convolutional Normalizing Flowscited by 13
  3. 2024Conditioned quantum-assisted deep generative surrogate for particle-calorimeter interactionscited by 4
  4. 2024Jet Discrimination with Quantum Complete Graph Neural Networkcited by 3
  5. 2026AllShowers: One model for all calorimeter showerscited by 3
  6. 2026Generalizable Foundation Models for Calorimetry via Mixtures-of-Experts and Parameter Efficient Fine Tuningcited by 3
  7. 2025A First Full Physics Benchmark for Highly Granular Calorimeter Surrogatescited by 2
  8. 2026Generative Models and Statistical Validationcited by 2
  9. 2024Zephyr quantum-assisted hierarchical Calo4pQVAE for particle-calorimeter interactionscited by 1
  10. 2026CaloTrilogy: Toward a Breakthrough in One-Step, End-to-End, Physics-Guided Shower Generation for Modern Calorimeterscited by 1
  11. 2026GPT-Based Fast Simulation of CLAS12 Detector Hits via Conditional Autoregressive Generationcited by 0