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Found 30 papers, 9 papers with code
Guided Quantum Compression for Higgs Identification
To ameliorate this issue, we design an architecture that unifies the preprocessing and quantum classification algorithms into a single trainable model: the guided quantum compression model.
Symmetry breaking in geometric quantum machine learning in the presence of noise
This work studies the behavior of EQNN models in the presence of noise.
Approximately Equivariant Quantum Neural Network for $p4m$ Group Symmetries in Images
Quantum Neural Networks (QNNs) are suggested as one of the quantum algorithms which can be efficiently simulated with a low depth on near-term quantum hardware in the presence of noises.
Hybrid Ground-State Quantum Algorithms based on Neural Schrödinger Forging
Entanglement forging based variational algorithms leverage the bi-partition of quantum systems for addressing ground state problems.
Assessment of few-hits machine learning classification algorithms for low energy physics in liquid argon detectors
The physics potential of massive liquid argon TPCs in the low-energy regime is still to be fully reaped because few-hits events encode information that can hardly be exploited by conventional classification algorithms.
Trainability barriers and opportunities in quantum generative modeling
In this work, we investigate the barriers to the trainability of quantum generative models posed by barren plateaus and exponential loss concentration.
Quantum anomaly detection in the latent space of proton collision events at the LHC
The designed quantum anomaly detection models, namely an unsupervised kernel machine and two clustering algorithms, are trained to find new-physics events in the latent representation of LHC data produced by the autoencoder.
Unravelling physics beyond the standard model with classical and quantum anomaly detection
Much hope for finding new physics phenomena at microscopic scale relies on the observations obtained from High Energy Physics experiments, like the ones performed at the Large Hadron Collider (LHC).
The Quantum Path Kernel: a Generalized Quantum Neural Tangent Kernel for Deep Quantum Machine Learning
A key issue is how to address the inherent non-linearity of classical deep learning, a problem in the quantum domain due to the fact that the composition of an arbitrary number of quantum gates, consisting of a series of sequential unitary transformations, is intrinsically linear.
Conditional Progressive Generative Adversarial Network for satellite image generation
In this work, we formulate the image generation task as completion of an image where one out of three corners is missing.
Running the Dual-PQC GAN on noisy simulators and real quantum hardware
In an earlier work, we introduced dual-Parameterized Quantum Circuit (PQC) Generative Adversarial Networks (GAN), an advanced prototype of a quantum GAN.
Conditional Born machine for Monte Carlo event generation
So called Born machines are purely quantum models and promise to generate probability distributions in a quantum way, inaccessible to classical computers.
Quantum neural networks force fields generation
Accurate molecular force fields are of paramount importance for the efficient implementation of molecular dynamics techniques at large scales.
Dual-Tasks Siamese Transformer Framework for Building Damage Assessment
Considering the frontier advances of Transformer architecture in the computer vision field, in this paper, we present the first attempt at designing a Transformer-based damage assessment architecture (DamFormer).
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Extracting Buildings In Remote Sensing Images
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Disaster Response
Extracting Buildings In Remote Sensing Images
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Accelerating GAN training using highly parallel hardware on public cloud
With the increasing number of Machine and Deep Learning applications in High Energy Physics, easy access to dedicated infrastructure represents a requirement for fast and efficient R&D.
Hybrid Quantum Classical Graph Neural Networks for Particle Track Reconstruction
This increase in luminosity will significantly increase the number of particles interacting with the detector.
Physics Validation of Novel Convolutional 2D Architectures for Speeding Up High Energy Physics Simulations
Our results demonstrate a high physics accuracy and further consolidate the use of GANs for fast detector simulations.
Higgs analysis with quantum classifiers
We have developed two quantum classifier models for the $t\bar{t}H(b\bar{b})$ classification problem, both of which fall into the category of hybrid quantum-classical algorithms for Noisy Intermediate Scale Quantum devices (NISQ).
Dual-Parameterized Quantum Circuit GAN Model in High Energy Physics
Generative models, and Generative Adversarial Networks (GAN) in particular, are being studied as possible alternatives to Monte Carlo simulations.
Reduced Precision Strategies for Deep Learning: A High Energy Physics Generative Adversarial Network Use Case
We employ the novel Intel low precision optimization tool (iLoT) for quantization and compare the results to the quantized model from TensorFlow Lite.
Pandemic Drugs at Pandemic Speed: Infrastructure for Accelerating COVID-19 Drug Discovery with Hybrid Machine Learning- and Physics-based Simulations on High Performance Computers
The race to meet the challenges of the global pandemic has served as a reminder that the existing drug discovery process is expensive, inefficient and slow.
Deep Learning strategies for ProtoDUNE raw data denoising
In this work, we investigate different machine learning-based strategies for denoising raw simulation data from the ProtoDUNE experiment.
Quantum Generative Adversarial Networks in a Continuous-Variable Architecture to Simulate High Energy Physics Detectors
We introduce and analyze a new prototype of quantum GAN (qGAN) employed in continuous-variable (CV) quantum computing, which encodes quantum information in a continuous physical observable.
Performance of Particle Tracking Using a Quantum Graph Neural Network
The Large Hadron Collider (LHC) at the European Organisation for Nuclear Research (CERN) will be upgraded to further increase the instantaneous rate of particle collisions (luminosity) and become the High Luminosity LHC.
Quantum Physics
A Quantum Graph Neural Network Approach to Particle Track Reconstruction
Unprecedented increase of complexity and scale of data is expected in computation necessary for the tracking detectors of the High Luminosity Large Hadron Collider (HL-LHC) experiments.
Quantum Physics
Particle Track Reconstruction with Quantum Algorithms
In addition, the ambiguity in assigning hits to particle tracks will be increased due to the finite resolution of the detector and the physical closeness of the hits.
Quantum Physics High Energy Physics - Experiment Data Analysis, Statistics and Probability
Calorimetry with Deep Learning: Particle Simulation and Reconstruction for Collider Physics
These networks can serve as fast and computationally light methods for particle shower simulation and reconstruction for current and future experiments at particle colliders.
Deploying AI Frameworks on Secure HPC Systems with Containers
The increasing interest in the usage of Artificial Intelligence techniques (AI) from the research community and industry to tackle "real world" problems, requires High Performance Computing (HPC) resources to efficiently compute and scale complex algorithms across thousands of nodes.
Machine Learning in High Energy Physics Community White Paper
In this document we discuss promising future research and development areas for machine learning in particle physics.
BIG-bench Machine Learning
Vocal Bursts Intensity Prediction
A Roadmap for HEP Software and Computing R&D for the 2020s
Particle physics has an ambitious and broad experimental programme for the coming decades.
Computational Physics High Energy Physics - Experiment
