The increasing demands on simulation statistics for HL-LHC analyses challenge the scalability of traditional calorimeter simulation within all LHC collaborations. Fast simulation techniques based on machine learning have proven effective, yet further improvement may arise from quantum-inspired models. In this study we investigate the feasibility of integrating Quantum Neural Network (QNN) components into the ATLAS fast calorimeter simulation framework, to evaluate potential gain in generative performance and generalisation. We focus on a hybrid pipeline where a quantum Generative Adversarial Network (qGAN) is employed, whose generator is composed of a Parametrised Quantum Circuit (PQC) followed by a classical neural network, while the discriminator is kept classical. We report on implementation details, benchmarking against established fast simulation components, and performance metrics. Preliminary results show that qGAN models are technically feasible within ATLAS software and are promising for benefit with respect to the classical ATLAS fast simulation system.
The increasing demands on simulation statistics for HL-LHC analyses challenge the scalability of traditional calorimeter simulation within all LHC collaborations. Fast simulation techniques based on machine learning have proven effective, yet further improvement may arise from quantum-inspired models. In this study we investigate the feasibility of integrating Quantum Neural Network (QNN) components into the ATLAS fast calorimeter simulation framework, to evaluate potential gain in generative performance and generalisation. We focus on a hybrid pipeline where a quantum Generative Adversarial Network (qGAN) is employed, whose generator is composed of a Parametrised Quantum Circuit (PQC) followed by a classical neural network, while the discriminator is kept classical. We report on implementation details, benchmarking against established fast simulation components, and performance metrics. Preliminary results show that qGAN models are technically feasible within ATLAS software and are promising for benefit with respect to the classical ATLAS fast simulation system.
The fast calorimeter simulation is a critical computational bottleneck for the ATLAS experiment atthe High-Luminosity LHC and for the design of future colliders. Invertible Neural Networks (INNs) andnormalizingflowshavedemonstratedimpressiveaccuracyasgenerativemodelsforcalorimeter shower simulation, forming the backbone of the emerging FastCaloSim framework. In this work we explore the extension of this classical approach toward quantum-conditioned invertible neural networks (qINN). We present the conceptual motivation, discuss the fundamental tension between the unitarity of quantum circuits and the irreversibility of projective measurement, and out line two complementary solution strategies. As a first feasibility study we implement a hybrid architectureinwhichasmallquantumcircuitreplacestheclassicalconditioningsub-network inside an otherwise classicalINN, trained on ATLAS Open-Data pion showers from the FastCalorimeter Challenge dataset. The preliminary results, obtained with a 6-qubit, 2-layer variational ansatz, show encouraging convergence and motivate further optimisation. The work is a feasibility study; no definitive performance claims are made.
Quantum Computing has emerged as a viable alternative to classical computing for a number of different tasks, with the idea of a potential speedup in mind. One of the most promising fields of research in quantum computing is Quantum Machine Learning. An interesting area of application for Quantum Machine Learning can be found in High Energy Physics: one key component of creating experiments in a particle accelerator like the Large Hadron Collider is the simulation of those experiments beforehand, in order to gain a better understanding of the results actually measured. Those types of simulations, typically based on Monte-Carlo methods, require significant computing resources and, as we approach CERN's High Luminosity Phase, there is a strong need for more efficient and cost controlled simulations. In this thesis a model based on Quantum Generative Adversarial Networks (QGANs) is implemented, with the intention of exploring potential quantum computing advantages within a hybrid machine learning model that is tasked with the generation of calorimeter samples. The model is initially trained on an ideal simulator to ensure that the results are comparable to those of a similar classical model. Finally, the model is modified in order to effectively run on a real quantum device and the training is executed in its entirety on a 54-qubit quantum processor.
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.
This paper investigates the application of Quantum Generative Adversarial Networks (QGANs) to data augmentation for machine learning. Traditional Generative Adversarial Networks (GANs) rely on classical neural networks, while this research explores leveraging quantum circuits as generators within a QGAN architecture. The core idea is to train a quantum generator to produce synthetic data samples, supplementing existing datasets with novel instances. The discriminator remains a classical neural network, tasked with distinguishing between real and generated data. We present a theoretical framework for this approach, outlining the mathematical formulation and key components. Specifically, we introduce the notation (x, s_1, s_2, ..., s_K) to represent data samples, generator states, and relevant parameters. The objective function for the QGAN is defined as minimizing the Jensen-Shannon divergence between the real and generated data distributions. The effectiveness of this approach is discussed, highlighting potential advantages and challenges. This work contributes to the emerging field of quantum machine learning and offers a novel strategy for improving the performance of machine learning models, particularly in scenarios with limited training data.
Abstract The prospect of quantum computing with a potential exponential speed-up compared to classical computing identifies it as a promising method in the search for alternative future High Energy Physics (HEP) simulation approaches. HEP simulations, such as employed at the Large Hadron Collider at CERN, are extraordinarily complex and require an immense amount of computing resources in hardware and time. For some HEP simulations, classical machine learning models have already been successfully developed and tested, resulting in several orders of magnitude speed-up. In this research, we proceed to the next step and explore whether quantum computing can provide sufficient accuracy, and further improvements, suggesting it as an exciting direction of future investigations. With a small prototype model, we demonstrate a full quantum Generative Adversarial Network (GAN) model for generating downsized eight-pixel calorimeter shower images. The advantage over previous quantum models is that the model generates real individual images containing pixel energy values instead of simple probability distributions averaged over a test sample. To complete the picture, the results of the full quantum GAN model are compared to hybrid quantum-classical models using a classical discriminator neural network.
Quantum Generative Adversarial Networks (QGANs), as a rising paradigm in Quantum Machine Learning, have shown promising potential in image generation and processing. However, their output quality remains suboptimal, and existing research is largely limited to small-scale, proof-of-concept studies. In this work, we propose a hybrid quantum-classical GAN architecture, where the generator integrates parameterized quantum circuits (PQCs) and classical neural networks. This integration significantly enhances the visual quality of generated images. Our model leverages the complementary strengths of quantum and classical components and outperforms existing methods (Tsang et al. in IEEE Trans. Quantum Eng. 4:1–19, 2023 ; Gulrajani et al. in Proceedings of the 31st international conference on neural information processing systems. NIPS’17, Red Hook, pp. 5769–5779, 2017 ), particularly in terms of image fidelity. Experiments conducted on the MNIST family of datasets show that our hybrid approach achieves a 20.26% average reduction in Fréchet Inception Distance. Furthermore, it improves the Structural Similarity Index Measure, Cosine Similarity, and Peak Signal-to-Noise Ratio by 26.04%, 2.22%, and 7.62%, respectively. These results highlight the effectiveness of combining quantum computing with machine learning, and underscore the potential of hybrid quantum-classical models in advancing generative tasks.