Quantum Generative Modeling for Calorimeter Simulations in Noisy Quantum Device

European Physical Society Conference on High Energy Physics (EPS-HEP), pp. 639

Abstract

Quantum-based generative models offer a promising approach for simulating complex phenomena in high-energy physics, such as calorimeter showers used for particle identification and energy reconstruction at the LHC. We propose the Quantum Angle Generator (QAG), a variational quantum model trained with a Maximum Mean Discrepancy loss to generate images from the probabilistic outputs of quantum circuits. We study model and training hyperparameters, assess the impact of quantum noise during training and inference, and evaluate the QAG in both noiseless simulations and simulated Noisy Intermediate-Scale Quantum hardware. Results show that the QAG can adapt through learning to hardware-induced noise, yielding stable, high-quality outputs even under significant noise and calibration variability.

Authors 6

1. Saverio Monaco

   Deutsches Elektronen-Synchrotron DESY · European Organization for Nuclear Research

   Affiliation as printed

   𝑎 DESY , Notkestraße 85 , Hamburg , Germany 𝑏

   𝑐 CERN , Esplanade des Particules 1 , Meyrin , Switzerland

2. Florian Rehm Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH University , Templergraben 55 , Aachen , Germany

3. M. Scham Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH University , Templergraben 55 , Aachen , Germany

4. Dirk Krücker

   Deutsches Elektronen-Synchrotron DESY

   Affiliation as printed

   𝑎 DESY , Notkestraße 85 , Hamburg , Germany 𝑏

5. K. Borras Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH University , Templergraben 55 , Aachen , Germany

6. J. Slim Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH University , Templergraben 55 , Aachen , Germany