Snowmass White Paper: Quantum Computing Systems and Software for High-energy Physics Research

Humble, Travis S.; Delgado, A.; Pooser, Raphael C.; Seck, Christopher M.; Bennink, Ryan S.; Leyton-Ortega, Vicente; Joseph, Wang, C. -C.; Dumitrescu, Eugene; Morris, Titus; Hamilton, Kathy; Lyakh, Dmitry I.; Date, Prasanna; Wang, Yan; Peters, Nicholas A.; Evans, Katherine J.; Demarteau, M.; McCaskey, Alex; Nguyen, Thien; Clark, Susan; Reville, Melissa; Meglio, Alberto Di; Grossi, Michele; Vallecorsa, S.; Borras, K.; Jansen, Karl; Krücker, D.

doi:10.48550/arxiv.2203.07091

Cited by 4 publications

(3 citation statements)

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“…As QC has great potential for simulation in NHEP [34], among the co-design parameters, the number of qubits is particularly important for the simulation of large quantum systems. Another promising direction could be the analysis of events [35] or the real-time control of experiments [9] by quantum machine learning or optimisation techniques.…”

Section: Quantum Hardware Designsmentioning

confidence: 99%

Co-Design of Quantum Hardware and Algorithms in Nuclear and High Energy Physics

Franz,

Zurita,

Diefenthaler

et al. 2024

EPJ Web of Conf.

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Quantum computing (QC) has emerged as a promising technology, and is believed to have the potential to advance nuclear and high energy physics (NHEP) by harnessing quantum mechanical phenomena to accelerate computations. In this paper, we give a brief overview of the current state of quantum computing by highlighting challenges it poses and opportunities it offers to the NHEP community. Noisy intermediate-scale quantum (NISQ) computers, while limited by imperfections and small scale, may hold promise for near-term quantum advantages when coupled with co-designed quantum algorithms and special-purpose quantum processing units (QPUs). We explore various applications in NHEP, including quantum simulation, event classification, and realtime experiment control, emphasising the potential of variational quantum circuits and related techniques. To identify current interests of the community, we perform an analysis of recent literature in NHEP related to QC.

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Section: Quantum Hardware Designsmentioning

confidence: 99%

Co-Design of Quantum Hardware and Algorithms in Nuclear and High Energy Physics

Franz,

Zurita,

Diefenthaler

et al. 2024

EPJ Web of Conf.

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“…In this paper, QML refers to machine learning tasks that are executed on quantum computing hardware. While QML is not known to be more efficient than classical machine learning (CML), there have been many empirical studies to explore the potential of QML for HEP [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19] (see also ref. [20] for a recent review).…”

Section: Introductionmentioning

confidence: 99%

Quantum anomaly detection for collider physics

Alvi

Bauer

Nachman

2023

J. High Energ. Phys.

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We explore the use of Quantum Machine Learning (QML) for anomaly detection at the Large Hadron Collider (LHC). In particular, we explore a semi-supervised approach in the four-lepton final state where simulations are reliable enough for a direct background prediction. This is a representative task where classification needs to be performed using small training datasets — a regime that has been suggested for a quantum advantage. We find that Classical Machine Learning (CML) benchmarks outperform standard QML algorithms and are able to automatically identify the presence of anomalous events injected into otherwise background-only datasets.

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“…[20] for a review. It has further propelled progress in theory, algorithm, and codesign [9,10,[20][21][22][23][24][25][26][27][28][29][30][31][32].…”

Section: Introductionmentioning

confidence: 99%