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CaloQVAE for electromagnetic shower synthetic data generation in the ATLAS calorimeter

Wojciech T. Fedorko

Published Sep 25, 2026• 0 citations

Abstract

As CERN approaches the High-Luminosity LHC (HL-LHC) run, the computational cost of first-principles detector simulation has become a critical bottleneck, with projections indicating millions of CPU-years required to generate the necessary Monte Carlo datasets — a substantial share of which is consumed by calorimeter shower simulation. Building on our earlier proof-of-concept demonstrating Quantum-assisted Generative AI for synthetic calorimetry data generation, we present what is, to our knowledge, the first full-scale demonstration of this approach applied to ATLAS detector simulated data. Our model couples a Restricted Boltzmann Machine (RBM), mapped onto the logical qubit topology of D-Wave's quantum annealing processor, with a deep generative architecture to produce realistically scaled calorimeter showers. We extend our earlier framework with several techniques critical to scaling the approach to production-grade ATLAS geometry and granularity: advanced prior conditioning strategies, Spin Reversal Transforms to mitigate intrinsic qubit biases, orbit averaging to reduce sampling noise, and QPU-aware weight normalization to stabilize the sampling process. We show how these advances yield quantum processor generated calorimeter datasets that maintain high fidelity in physics features relative to first-principles GEANT4 simulation while promising substantial improvements in generation throughput over both traditional simulation and purely classical deep learning generative models. We discuss the implications of these results for scaling quantum-assisted generative modeling to full ATLAS fast-simulation pipelines ahead of the HL-LHC era.

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