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

Area of Science:

• High-Energy Physics
• Computational Physics
• Machine Learning

Background:

• The Large Hadron Collider's high luminosity era demands extensive computational resources for event analysis.
• Accurate Monte Carlo (MC) simulations are crucial for reducing statistical uncertainties in experimental data.
• Simulating particle interactions within calorimeters is a major computational bottleneck in MC event generation.

Purpose of the Study:

• To develop a faster and more efficient method for simulating high-energy particle interactions in detector calorimeters.
• To address the computational challenges posed by the increasing data volume from the Large Hadron Collider.

Main Methods:

• Integration of advanced generative models for pattern recognition and prediction.
• Application of quantum annealing algorithms to optimize complex simulation tasks.
• Combined approach for simulating particle propagation through detector calorimeters.

Main Results:

• Demonstrated significant speed-up in the simulation of particle-calorimeter interactions.
• Achieved efficient generation of simulated datasets required for high-LHC analysis.
• Validated the accuracy of the proposed simulation technique against standard methods.

Conclusions:

• The proposed technique offers a promising solution for accelerating computationally demanding MC simulations in high-energy physics.
• Combining generative models and quantum annealing can overcome current limitations in simulating particle detector responses.
• This advancement will facilitate more precise data analysis and discovery potential at the Large Hadron Collider.