Quantum optimisation for grid computing

Project goal

The goal of this project is to develop quantum algorithms to help optimise how data is distributed for storage in the Worldwide LHC Computing Grid (WLCG), which consists of 170 computing centres, spread across 42 countries. Initial work focuses on the specific case of the ALICE experiment. We are trying to determine the optimal storage, movement, and access patterns for the data produced by this experiment in quasi-real-time. This would improve resource allocation and usage, thus leading to increased efficiency in the broader data-handling workflow.

R&D topic

Quantum technologies

Project coordinator(s)

Federico Carminati

Team members

Politehnica Univerity of Bucharest: Mircea-Marian Popa, Mihai Carabas, Popescu George Pantelimon │ Institut Polytechnique de Grenoble: Jaques Demongeot │ CERN openlab: Sofia Vallecorsa, Fabio Fracas │ CERN, ALICE: Costin Grigoras, Latchezar Betev

Collaborators

Project background

The WLCG has been essential to the success of the LHC’s scientific programme. It is used to store and analyse the data produced by the LHC experiments. Optimal usage of the grid’s resources is a major challenge: with the foreseen increase in the data produced by the LHC experiments, workflow optimisation — particularly for the data-placement strategy — becomes extremely important.

Simulating this complex and highly non-linear environment is very difficult; the complexity of the task goes beyond the capability of the computing hardware available today. Quantum computing could offer the possibility to address this. Our project, a collaboration with Google, the Polytechnic Institute of Grenoble and the Polytechnic University of Bucharest, will develop quantum algorithms to optimise the storage distribution.

Recent progress

In May, this project was awarded one-year funding under the European Union’s ATTRACT initiative. ATTRACT provides initial funding to 170 disruptive projects, each aiming to develop sensing and imaging technologies that will enable breakthrough innovations. This project, which has the full title of ‘Quantum optimisation of Worldwide LHC Computing Grid data placement’ is one of 19 projects funded in which CERN is involved. One of the major challenges faced by this project is the difficulty of defining a suitable description of the data set extracted from monALISA, the monitoring and scheduling tool used by the ALICE experiment for grid operations. We have now defined the problem in terms of reinforcement learning, one of the three paradigms of machine learning (alongside supervised and unsupervised learning). We have also started implementing the key components of the reinforcement learning framework (in terms of environment and agent networks) that is to be used.

Next steps

Currently the deep neural network describing the environment and the agent behaviours are being implemented as classical networks. Our first goal is to prove that this strategy can reproduce the expected monAlisa behaviour. At a later stage we will implement these components as quantum circuits using CIRQ and work on the optimisation of the training process. Quantum computing and grid/cloud computing are game-changing technologies with the potential to have a very large impact on the future. Our results will provide an excellent initial prototype: the work could be then extended and integrated with other existing initiatives at the scale of the whole Worldwide LHC Computing Grid. Eventually benefits in terms efficient network usages, reduced computing time, optimised storage and therefore costs would be significant.

Presentations

F. Carminati, Quantum Optimization of Worldwide LHC Computing Grid data placement (7 November). Presented at Conference on Computing in High Energy & Nuclear Physics (CHEP), Adelaide, 2019. http://cern.ch/go/nP9M

Quantum optimisation for grid computing