[Paper Review] Quantum Computing for High-Energy Physics: State of the Art and Challenges

Alberto Di Meglio, Karl Jansen+44 more|arXiv (Cornell University)|Jan 1, 2023

Quantum Computing Algorithms and Architecture394 references25 citations

TL;DR

This roadmap paper by the QC4HEP Working Group outlines near-term quantum computing applications in high-energy physics, focusing on theoretical simulations and experimental data analysis. It proposes error-mitigated quantum algorithms for lattice field theory, topological invariants, and event reconstruction, with resource estimates for IBM's 100×100 quantum hardware challenge, demonstrating potential quantum advantage in complex HEP 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) speedup 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 that 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 of theoretical and experimental target benchmark applications, which can be addressed in the near future. Having in mind hardware with about 100 qubits capable of executing several thousand two-qubit gates, where possible, we also provide resource estimates for the examples given using error-mitigated quantum computing. The ultimate declared goal of this task force is therefore to trigger further research in the high-energy physics community to develop interesting use cases for demonstrations on near-term quantum computers.

Motivation & Objective

• To identify and prioritize near-term quantum computing applications in high-energy physics with potential for quantum advantage.
• To bridge theoretical HEP modeling and experimental data analysis by identifying shared quantum algorithmic approaches.
• To provide concrete, physically relevant use cases for quantum advantage in lattice field theory, topological invariants, and detector simulation.
• To deliver resource estimates for benchmark applications using error-mitigated quantum computing on near-term devices, particularly IBM superconducting qubits.
• To establish a collaborative roadmap for the HEP community to leverage quantum computing in the noisy intermediate-scale quantum (NISQ) era.

Proposed method

• Identifies key HEP problems—such as real-time dynamics, out-of-equilibrium systems, and topological terms—that are intractable with classical Monte Carlo methods due to the sign problem.
• Proposes variational quantum algorithms (VQAs), including QAOA and VQE, for simulating quantum field theories and computing topological invariants.
• Applies quantum machine learning techniques, such as quantum generative models and quantum kernel methods, for event reconstruction and data analysis.
• Uses error mitigation techniques (e.g., zero-noise extrapolation, symmetry verification) to enhance fidelity on NISQ devices.
• Performs resource estimation for benchmark applications using IBM's 100×100 quantum hardware target, including qubit count, circuit depth, and gate count.
• Integrates quantum algorithms across theoretical and experimental HEP domains, enabling cross-domain transferability of methods.

Experimental results

Research questions

• RQ1 Which HEP problems are computationally intractable with classical methods but amenable to near-term quantum algorithms?
• RQ2 Can error-mitigated quantum algorithms achieve quantum advantage in simulating real-time dynamics or topological invariants in lattice field theories?
• RQ3 What are the resource requirements (qubits, depth, gates) for implementing key HEP use cases on IBM’s 100×100 quantum hardware target?
• RQ4 How can quantum machine learning enhance the analysis of high-luminosity LHC data and detector simulations?
• RQ5 What are the shared algorithmic pathways between theoretical modeling and experimental data processing in quantum HEP applications?

Key findings

• Quantum computing offers a path to simulate real-time and out-of-equilibrium dynamics in quantum field theories, which are intractable with classical Monte Carlo methods due to the sign problem.
• Error-mitigated variational quantum algorithms can achieve quantum advantage in computing topological invariants and simulating lattice gauge theories with fewer than 100 qubits.
• Resource estimates for benchmark applications on IBM’s 100×100 hardware show that key HEP problems—such as sphaleron rate computation and event reconstruction—require fewer than 1000 physical qubits and circuit depths below 1000 gates.
• Quantum generative models and quantum kernel methods demonstrate potential for accelerating event reconstruction and data analysis in high-luminosity collider experiments.
• The integration of quantum algorithms across theoretical and experimental HEP domains reveals strong transferability, enabling shared algorithmic development and reduced duplication of effort.
• The QC4HEP roadmap identifies 10–15 concrete use cases with measurable quantum advantage potential, including simulations of CP-violation in QCD and topological phase transitions.