Summary of Conditioned Quantum-assisted Deep Generative Surrogate For Particle-calorimeter Interactions, by J. Quetzalcoatl Toledo-marin et al.
Conditioned quantum-assisted deep generative surrogate for particle-calorimeter interactions
by J. Quetzalcoatl Toledo-Marin, Sebastian Gonzalez, Hao Jia, Ian Lu, Deniz Sogutlu, Abhishek Abhishek, Colin Gay, Eric Paquet, Roger Melko, Geoffrey C. Fox, Maximilian Swiatlowski, Wojciech Fedorko
First submitted to arxiv on: 30 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); High Energy Physics – Phenomenology (hep-ph); Computational Physics (physics.comp-ph); Instrumentation and Detectors (physics.ins-det)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed conditioned quantum-assisted deep generative model integrates a conditioned variational autoencoder (VAE) and a conditioned Restricted Boltzmann Machine (RBM) to enhance expressiveness in simulating Large Hadron Collider (LHC) collisions. The model utilizes D-Wave’s Pegasus-structured Advantage quantum annealer for sampling, conditioned using flux biases. The framework is demonstrated on Dataset 2 of the CaloChallenge. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Scientists have created a new way to simulate particle collisions at accelerators like the Large Hadron Collider. This helps them understand the Standard Model and search for new things. However, this process uses a lot of computer power and takes a long time. To solve this problem, researchers developed a special model that combines two other models: a variational autoencoder (VAE) and a Restricted Boltzmann Machine (RBM). This new model uses a quantum computer to help with the simulation. It’s like having a super-powerful calculator! |
Keywords
» Artificial intelligence » Generative model » Variational autoencoder
