PHAT-JeT combines geometric message-passing with hierarchical patch attention to reach state-of-the-art accuracy and background rejection among resource-constrained jet tagging models on four benchmarks.
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A 10-qubit convolutional quantum graph neural network fed by autoencoder-compressed jet data achieves performance comparable to classical graph networks in distinguishing boosted Z jets from gluon jets.
SAL-T enhances the linformer with spatially aware kinematic partitioning and convolutions to match full-attention transformer performance on jet tagging while keeping linear complexity and lower latency.
Parametrized quantum circuit anomaly detector trained on classical hardware and tested on IBM devices for handwritten digits and simulated long-lived particle signals in HEP, but does not outperform classical deep neural networks due to noise and amplitude encoding requirements.
A 500-logical-qubit quantum computer could reject laboratory-confined theories by surpassing the Planck-scale operation rate of 2^491 m^{-3} s^{-1}, with a 1600-qubit machine limited by the observable universe.
This paper overviews the LHCb Stripping framework's Python-based architecture, GitLab workflows, automation, and roadmap for processing both legacy and new high-energy physics data.
citing papers explorer
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Patch Hierarchical Attention Transformer for Efficient Particle Jet Tagging
PHAT-JeT combines geometric message-passing with hierarchical patch attention to reach state-of-the-art accuracy and background rejection among resource-constrained jet tagging models on four benchmarks.
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Quantum enhanced identification of boosted jets with quantum graph neural networks
A 10-qubit convolutional quantum graph neural network fed by autoencoder-compressed jet data achieves performance comparable to classical graph networks in distinguishing boosted Z jets from gluon jets.
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Spatially Aware Linear Transformer (SAL-T) for Particle Jet Tagging
SAL-T enhances the linformer with spatially aware kinematic partitioning and convolutions to match full-attention transformer performance on jet tagging while keeping linear complexity and lower latency.
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Long-lived Particles Anomaly Detection with Parametrized Quantum Circuits
Parametrized quantum circuit anomaly detector trained on classical hardware and tested on IBM devices for handwritten digits and simulated long-lived particle signals in HEP, but does not outperform classical deep neural networks due to noise and amplitude encoding requirements.
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Probing the Planck scale with quantum computation
A 500-logical-qubit quantum computer could reject laboratory-confined theories by surpassing the Planck-scale operation rate of 2^491 m^{-3} s^{-1}, with a 1600-qubit machine limited by the observable universe.
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The LHCb Stripping Project: Sustainable Legacy Data Processing for High-Energy Physics
This paper overviews the LHCb Stripping framework's Python-based architecture, GitLab workflows, automation, and roadmap for processing both legacy and new high-energy physics data.