There is a large gap between generic quantum computing algorithms and their application to these data-intensive sciences like the LHC experiments - an issue that requires not only knowledge of quantum computing algorithms, but also of physics processes in the LHC and which part of the data analysis is most appropriately tackled by specific quantum algorithms: particle tracking and identification, photon ray tracing, or the generation of realistic reference data sets through simulation of quantum physical processes - key issues also identified by the CERN Quantum Technology Initiative (QTI) as those most relevant to impact the processing of large-scale high energy physics data. Within the SURF Quantum Computing innovation agenda, addressing some these challenges would address the long-term goal of making efficient and effective use of computing facilities, it matches well the existing work in other SURF quantum innovation projects such as those for raytracing in atmospheric simulation, and optimally exploits the new HPC capabilities at SURF for quantum computing simulation in the presence of large-volume datasets.

SURF and LHCb - quantum computing for data intensive science

The project aims to establish a knowledge base at SURF and in the Netherlands and to act as a proof-of-concept environment to experiment with novel algorithms at a scale that is realistic to identify computational and algorithmic bottlenecks. It initially addresses algorithm development and its execution on quantum simulators, but with the specific aim to validate the algorithm where possible on general-purpose NISQ quantum computers (e.g. in collaboration with IBM) and prepare the way to experiment with these solutions on the Demonstrators in the context of the Action Line 1 of Quantum Delta NL.

This project pursues three goals:

  • Pattern recognition for particle tracks in LHCb
  • Ray Tracing of photons in the LHCb RICH
  • Disseminating experience and knowledge to SURF members

The project will run from February 2022 till April 2023, and is run by Nikhef partner Maastricht University. The project lead is Jacco de Vries (Nikhef/UM), with Ariana Torres (SURF), alongside Daniel Campora (UM/Nikhef), Kareljan Schoutens (UvA), and David Groep (Nikhef).