AI RCS Strategy Workshop

Session

Experimental Technologies

15 Sept 2025, 16:00

500/1-001 - Main Auditorium (CERN)

98. Simplicial attention mechanism for physics objects

Sebastian Wuchterl (CERN)

15/09/2025, 16:00

7. Experimental Technologies

While the usual attention mechanism successfully introduced edge features allowing to compute efficiently the inter-connection between two elements, one could consider more-object connections via a simplex system, which would generalize the concept of attention to any higher dimension, allowing a “hyper-graph” like attention model; see, e.g., arXiv:2309.02138.

93. Robust and Efficient Topological Deep Learning for Event Reconstruction

Christine Zeh (Vienna University of Technology (AT))

15/09/2025, 16:05

7. Experimental Technologies

Event reconstruction at the HL-LHC requires combining hits into clusters and linking them with tracks to form higher-level objects. This process is inherently multi-step and local, which risks globally suboptimal results when pile-up is high or when showers overlap. Current machine learning methods, like graph neural networks and transformers, already exploit relational structures, with recent...

124. Accelerating AI with in-memory computing devices

Maurizio Pierini (CERN)

15/09/2025, 16:10

7. Experimental Technologies

Energy efficiency, while lowering the barrier to incorporating emerging device technologies into muture generations of computing systems must achieve higher processing speed and energy efficiency to support rapidly growing workloads under strict environmental constraints. To address this, domain-specific hardware accelerators have gained traction, with in-memory computing (IMC) emerging as a...

123. Tensor Networks for Particle Physics

Ms Ema Puljak (The Barcelona Institute of Science and Technology (BIST) (ES)), Maurizio Pierini (CERN)

15/09/2025, 16:15

7. Experimental Technologies

Develop applications based on tensor networks for LHC tasks. As part of this effort, develop the tn4ml library, to train TNs with tools used for deep learning applications.

90. QML at LHCb

Jacco Andreas De Vries (Nikhef National institute for subatomic physics (NL)), Dr Nicole Skidmore (University of Warwick)

15/09/2025, 16:20

7. Experimental Technologies

The advent of the High-Luminosity LHC presents unprecedented computational challenges for the LHCb experiment, pushing the limits of classical algorithms in areas such as real-time data filtering, complex track reconstruction, and multidimensional analysis. To address this we propose a dedicated initiative to expand upon LHCb’s pioneering application of Quantum Machine Learning (QML) for b-jet...

139. Hybrid Monte Carlo event generators

Dr Michele Grossi (CERN), Dr Sofia Vallecorsa (CERN)

15/09/2025, 16:25

7. Experimental Technologies

Monte Carlo (MC) event generators are indispensable in High-Energy Physics (HEP) for simulating scattering processes and sampling the multidimensional phase space according to the differential cross section dσ. Since dσ is not known analytically in full generality, event generators must determine both the local structure of the integrand and the global phase space distribution through adaptive...

140. Warm-starting Variational Quantum Algorithms via Pre-Training on Classically Simulable Structures

Jogi Suda Neto (University of Alabama (US))

15/09/2025, 16:30

7. Experimental Technologies

Variational quantum algorithms (VQAs) offer a promising approach for near-
term quantum devices but often suffer from trainability issues such as barren
plateaus. While certain VQAs can avoid these problems, they are typically
classically simulable and thus of limited quantum advantage. This project explores the use of pre-training as a warm-starting strategy for VQAs that are...