Quantum Computing for High-Energy Physics: State of the Art and Challenges. Summary of the QC4HEP Working Group

TL;DR

This paper reviews the current state and challenges of applying quantum computing to high-energy physics, highlighting recent developments, potential applications, and future resource estimates for quantum advantage in the field.

Contribution

It provides a comprehensive overview of the progress, challenges, and near-term applications of quantum computing in high-energy physics, including benchmark examples and resource assessments.

Findings

Quantum computers can potentially revolutionize high-energy physics simulations.

Current hardware enables small-scale but representative quantum applications.

Resource estimates suggest feasible near-term quantum advantage for specific problems.

Abstract

Quantum computers offer an intriguing path for a paradigmatic change of computing in the natural sciences and beyond, with the potential for achieving a so-called quantum advantage, namely a significant (in some cases exponential) speed-up of numerical simulations. The rapid development of hardware devices with various realizations of qubits enables the execution of small scale but representative applications on quantum computers. In particular, the high-energy physics community plays a pivotal role in accessing the power of quantum computing, since the field is a driving source for challenging computational problems. This concerns, on the theoretical side, the exploration of models which are very hard or even impossible to address with classical techniques and, on the experimental side, the enormous data challenge of newly emerging experiments, such as the upgrade of the Large Hadron Collider. In this roadmap paper, led by CERN, DESY and IBM, we provide the status of high-energy physics quantum computations and give examples for theoretical and experimental target benchmark applications, which can be addressed in the near future. Having the IBM 100 x 100 challenge in mind, where possible, we also provide resource estimates for the examples given using error mitigated quantum computing.