Today, researchers have published an important white paper identifying activities in particle physics where burgeoning quantum-computing technologies could be applied. The paper, authored by experts from CERN, DESY, IBM Quantum and over 30 other organisations, is now available on ArXiv.
With quantum-computing technologies rapidly improving, the paper sets out where these could be applied within particle physics, in order to help tackle computing challenges related not only to the Large Hadron Collider’s ambitious upgrade programme, but also to other colliders and low energy experiments world-wide.
The paper was produced by a working group set up at the first-of-its-kind “QT4HEP” conference, held at CERN last November. Over the last eight months, the 46 people in this working group have worked hard to identify areas where quantum-computing technologies could provide a significant boon.
The areas identified relate to both theoretical and experimental particle physics. The paper then maps these areas to “problem formulations” in quantum computing. This is an important step in ensuring that the particle physics community is well positioned to benefit from the massive potential of breakthrough new quantum computers when they come online.
“Quantum computing is very promising, but not every problem in particle physics is suited to this mode of computing,” says Alberto Di Meglio, head of the CERN Quantum Technology Initiative (CERN QTI). “It’s important to ensure that we are ready and that we can accurately identify the areas where these technologies have the potential to be most useful for our community.”
In terms of theoretical particle physics, the authors identify promising areas related to evolution of the quantum states, lattice-gauge theory, neutrino oscillations, and quantum field theories in general as well. The considered applications include quantum dynamics, hybrid quantum/classical algorithms for static problems in lattice gauge theory, optimisation, and classification. The lead authors of the paper CERN QTI’s Alberto Di Meglio, DESY’s Karl Jansen, and IBM Quantum’s Ivano Tavernelli, state that “with quantum computing we address problems in those areas that are very hard – or even impossible to tackle with classical methods. “In this way,” Jansen says, “we can explore the physical systems to which we still do not have access.”
On the experimental side, the authors identify areas related to jet and track reconstruction, extraction of rare signals, for-and-beyond Standard Model problems, parton showers, and experiment simulation. These are then mapped to classification, regression, optimisation, and generation problems.
Members of the working group behind this paper will now begin a process of selecting specific use cases from the activities listed in the paper to be taken forward through the CERN’s and DESY’s participation in the IBM Quantum Network, and collaboration with IBM Quantum, under its “100x100 Challenge”. IBM Quantum is long-standing collaborator to CERN QTI and the Center for Quantum Technologies and Applications (CQTA) at DESY.
IBM’s 100x100 Challenge will see the company provide a tool capable of calculating unbiased observables of circuits with 100 qubits and depth-100 gate operations in 2024. This will provide an important testbed for taking forward promising selected use cases, both from particle physics and other research fields.
The working group behind the paper will meet again at CERN for a special workshop on 16 and 17 November, immediately before the Quantum Techniques in Machine Learning Conference is held at the laboratory on 19-24 November.
“This white paper — and the many discussions we had as part of its creation — will be important in shaping the work to be carried out in CERN QTI’s second phase, which was recently given support by the CERN Council,” says Di Meglio, who also leads CERN’s new IT Innovation section. “I’d like to thank all of the world-leading experts who contributed to this paper, which provides a thorough assessment of the potential of this game-changing new technology for our field.”
Read the full paper on ArXiv here: https://cern.ch/quantumcomputingforhep