Conveners
1.4 Hardware acceleration & FPGAs
- Julián García Pardiñas (CERN)
Presentation materials
We investigate the possibility to apply quantum machine learning techniques for data analysis, with particular regard to an interesting use-case in high-energy physics. We propose an anomaly detection algorithm based on a parametrized quantum circuit. This algorithm was trained on a classical computer and tested with simulations as well as on real quantum hardware. Tests on NISQ devices were...
Estimating unknown parameters of open quantum systems is an important task that is common to many branches of quantum technologies, from metrology to computing. When open quantum systems are monitored and a signal is continuously acquired, this signal can be used to efficiently extract information about the interactions in the system. Previous works have demonstrated a Bayesian framework for...
We currently find ourselves in the era of noisy intermediate-scale quantum (NISQ) computing, where quantum computing applications are limited yet promising. In this work I will overview two algorithms for computing the ground state and dynamics of the transverse field Ising model as a testbed for more complex models. The Variational Quantum Eigensolver (VQE) algorithm leverages quantum...
Tracking charged particles in high-energy physics experiments is one of the most computationally demanding steps in the data analysis pipeline.
As we approach the High Luminosity LHC era, with an estimate increase in the number of proton-proton interactions per beam collision by a factor 3-5 (from 50 to 140-200 primary interactions per collision on average), particle tracking will become even...
Tensor Networks (TNs) is a computational paradigm used for representing quantum many-body systems. Recent works show how TNs can be applied to perform Machine Learning (ML) tasks, yielding comparable results to standard supervised learning techniques. In particular [1] leveraged Tree Tensor Networks (TTNs) to achieve the classification of particle flavor state in the context of High Energy...
The Large-Sized Telescope (LST) is one of three telescope types being built as part of the Cherenkov Telescope Array Observatory (CTAO) to cover the lower energy range between 20 GeV and 200 GeV. The Large-Sized Telescope prototype (LST-1), installed at the La Palma Observatory Roque de Los Muchachos, is currently being commissioned and has successfully taken data since November 2019. The...
Resource utilization plays a crucial role for successful implementation of fast real-time inference for deep neural networks on latest generation of hardware accelerators (FPGAs, SoCs, ACAPs, GPUs). To fulfil the needs of the triggers that are in development for the upgraded LHC detectors, we have developed a multi-stage compression approach based on conventional compression strategies...
Track finding in high-density environments is a key challenge for experiments at modern accelerators. In this presentation we describe the performance obtained running machine learning models for a typical Muon High Level Trigger at LHC experiments. These models are designed for hit position reconstruction and track pattern recognition with a tracking detector, on a commercially available...
In the realm of high-energy physics, the advent of machine learning has revolutionized data analysis, especially in managing the vast volumes of data produced by particle detectors.
Facing the challenge of analyzing unlabelled, high-volume detector data, advanced machine learning solutions become indispensable.
Our research introduces a machine learning approach that effectively bridges the...
Abstract: Large-scale physics experiments generating high data rates impose significant demands on the data acquisition system (DAQ). The Deep Underground Neutrino Experiment (DUNE) is a next-generation experiment for neutrino science at the Fermi National Accelerator Laboratory in Batavia, Illinois. It will consist of a massive detector operating continually for over a decade, resulting in...
