Speakers
Description
The increasing demands on simulation statistics for HL-LHC analyses challenge the scalability of traditional calorimeter simulation across all LHC collaborations. While machine learning based fast simulation techniques have demonstrated strong performance, future collider experiments will require generative models that are not only accurate and fast, but also scalable and interpretable in regimes of unprecedented detector granularity and event rates. In this context, quantum based generative models offer a novel direction.
In this work, we investigate the integration of Quantum Neural Network (QNN) architectures into the ATLAS fast calorimeter simulation framework (fastCaloSim), with the dual goal of improving generative performance and generalisation while maintaining practical inference times. We present a comparative study of two complementary approaches. The first adopts a hybrid quantum/classical strategy, where a quantum Generative Adversarial Network (qGAN) is used during training to learn the latent space of calorimeter shower representations, followed by classical neural networks for event generation. The second approach introduces a Quantum Invertible Neural Network (qINN), a generative architecture still largely unexplored especially in practical implementation, which provides a bijective mapping between input kinematic parameters and calorimeter shower observables, enabling explicit likelihood evaluation and enhanced interpretability.
Both approaches are implemented and benchmarked within fastCaloSim using ATLAS open-data samples, and evaluated in terms of generative fidelity and compatibility with ATLAS production workflows. Preliminary results demonstrate the feasibility of quantum based generative models for fast detector simulation and highlight the potential of qINN-based approaches as a novel paradigm for future event simulation at next generation collider experiments.