An official website of the United States government
Abstract Particle collisions at accelerators like the Large Hadron Collider (LHC), recorded by experiments such as ATLAS and CMS, enable precise standard model measurements and searches for new phenomena. Simulating these collisions significantly influences experiment design and analysis but incurs immense computational costs, projected at millions of CPU-years annually during the high luminosity LHC (HL-LHC) phase. Currently, simulating a single event with Geant4 consumes around 1000 CPU seconds, with calorimeter simulations especially demanding. To address this, we propose a conditioned quantum-assisted generative model, integrating a conditioned variational autoencoder (VAE) and a conditioned restricted Boltzmann machine (RBM). Our RBM architecture is tailored for D-Wave’s Pegasus-structured advantage quantum annealer for sampling, leveraging the flux bias for conditioning. This approach combines classical RBMs as universal approximators for discrete distributions with quantum annealing’s speed and scalability. We also introduce an adaptive method for efficiently estimating effective inverse temperature, and validate our framework on Dataset 2 of CaloChallenge.
more »
« less
Calo4pQVAE: Quantum-Assisted 4-Partite VAE Surrogate for High Energy Particle-Calorimeter Interactions
As we approach the High Luminosity Large Hadron Collider (HL-LHC) set to begin collisions by the end of this decade, it is clear that the computational demands of traditional collision simulations have become untenably high. Current methods, relying heavily on first-principles Monte Carlo simulations for event showers in calorimeters, are estimated to require millions of CPU-years annually, a demand that far exceeds current capabilities. This bottleneck presents a unique opportunity for breakthroughs in computational physics through the integration of generative AI with quantum computing technologies. We propose a Quantum-Assisted deep generative model. In particular, we combine a variational autoencoder (VAE) with a Restricted Boltzmann Machine (RBM) embedded in its latent space as a prior. The RBM in latent space provides further expressiveness compared to legacy VAE where the prior is a fixed Gaussian distribution. By crafting the RBM couplings, we leverage D-Wave’s Quantum Annealer to significantly speed up the shower sampling time. By combining classical and quantum computing, this framework sets a path towards utilizing large-scale quantum simulations as priors in deep generative models and demonstrate their ability to generate high-quality synthetic data for the HL-LHC experiments.
more »
« less
- PAR ID:
- 10651539
- Publisher / Repository:
- IEEE
- Date Published:
- Page Range / eLocation ID:
- 109 to 113
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract Deep generative learning cannot only be used for generating new data with statistical characteristics derived from input data but also for anomaly detection, by separating nominal and anomalous instances based on their reconstruction quality. In this paper, we explore the performance of three unsupervised deep generative models—variational autoencoders (VAEs) with Gaussian, Bernoulli, and Boltzmann priors—in detecting anomalies in multivariate time series of commercial-flight operations. We created two VAE models with discrete latent variables (DVAEs), one with a factorized Bernoulli prior and one with a restricted Boltzmann machine (RBM) with novel positive-phase architecture as prior, because of the demand for discrete-variable models in machine-learning applications and because the integration of quantum devices based on two-level quantum systems requires such models. To the best of our knowledge, our work is the first that applies DVAE models to anomaly-detection tasks in the aerospace field. The DVAE with RBM prior, using a relatively simple—and classically or quantum-mechanically enhanceable—sampling technique for the evolution of the RBM’s negative phase, performed better in detecting anomalies than the Bernoulli DVAE and on par with the Gaussian model, which has a continuous latent space. The transfer of a model to an unseen dataset with the same anomaly but without re-tuning of hyperparameters or re-training noticeably impaired anomaly-detection performance, but performance could be improved by post-training on the new dataset. The RBM model was robust to change of anomaly type and phase of flight during which the anomaly occurred. Our studies demonstrate the competitiveness of a discrete deep generative model with its Gaussian counterpart on anomaly-detection problems. Moreover, the DVAE model with RBM prior can be easily integrated with quantum sampling by outsourcing its generative process to measurements of quantum states obtained from a quantum annealer or gate-model device.more » « less
-
Szumlak, T; Rachwał, B; Dziurda, A; Schulz, M; vom_Bruch, D; Ellis, K; Hageboeck, S (Ed.)As CERN approaches the launch of the High Luminosity Large Hadron Collider (HL-LHC) by the decade’s end, the computational demands of traditional simulations have become untenably high. Projections show millions of CPU-years required to create simulated datasets - with a substantial fraction of CPU time devoted to calorimetric simulations. This presents unique opportunities for breakthroughs in computational physics. We show how Quantumassisted Generative AI can be used for the purpose of creating synthetic, realistically scaled calorimetry dataset. The model is constructed by combining D-Wave’s Quantum Annealer processor with a Deep Learning architecture, increasing the timing performance with respect to first principles simulations and Deep Learning models alone, while maintaining current state-of-the-art data qualitymore » « less
-
Deep neural networks are susceptible to shortcut learning, using simple features to achieve low training loss without discovering essential semantic structure. Contrary to prior belief, we show that generative models alone are not sufficient to prevent shortcut learning, despite an incentive to recover a more comprehensive representation of the data than discriminative approaches. However, we observe that shortcuts are preferentially encoded with minimal information, a fact that generative models can exploit to mitigate shortcut learning. In particular, we propose Chroma-VAE, a two-pronged approach where a VAE classifier is initially trained to isolate the shortcut in a small latent subspace, allowing a secondary classifier to be trained on the complementary, shortcut-free latent subspace. In addition to demonstrating the efficacy of Chroma-VAE on benchmark and real-world shortcut learning tasks, our work highlights the potential for manipulating the latent space of generative classifiers to isolate or interpret specific correlations.more » « less
-
The High-Luminosity Large Hadron Collider (HL-LHC) is set to introduce unprecedented data volumes and computational demands, necessitating significant enhancements in the current LHC computing infrastructure. We summarize efforts by the experiments to integrate high-performance computing clusters and public cloud resources into their processing frameworks. We also examine the adoption of cloud technologies for implementation of advanced service infrastructure which are finding applications in Tier 2 centers and prototyping of future analysis facilities. We highlight the crucial role of scalable networking capabilities and challenge exercises to prepare for the expected increased data throughput.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/QCE60285.2024.10262
