Quantum computing for data analysis in high energy physics

• URL: http://arxiv.org/abs/2203.08805v2
• Date: Thu, 8 Dec 2022 04:49:09 GMT
• Title: Quantum computing for data analysis in high energy physics
• Authors: Andrea Delgado, Kathleen E. Hamilton, Prasanna Date, Jean-Roch Vlimant, Duarte Magano, Yasser Omar, Pedrame Bargassa, Anthony Francis, Alessio Gianelle, Lorenzo Sestini, Donatella Lucchesi, Davide Zuliani, Davide Nicotra, Jacco de Vries, Dominica Dibenedetto, Miriam Lucio Martinez, Eduardo Rodrigues, Carlos Vazquez Sierra, Sofia Vallecorsa, Jesse Thaler, Carlos Bravo-Prieto, su Yeon Chang, Jeffrey Lazar, Carlos A. Arg\"uelles, Jorge J. Martinez de Lejarza
• Abstract summary: We provide an overview of the state-of-the-art applications of quantum computing to data analysis in High-Energy Physics. We discuss the challenges and opportunities in integrating these novel analysis techniques into a day-to-day analysis workflow.
• Score: 1.6002009406818865
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Some of the biggest achievements of the modern era of particle physics, such as the discovery of the Higgs boson, have been made possible by the tremendous effort in building and operating large-scale experiments like the Large Hadron Collider or the Tevatron. In these facilities, the ultimate theory to describe matter at the most fundamental level is constantly probed and verified. These experiments often produce large amounts of data that require storing, processing, and analysis techniques that often push the limits of traditional information processing schemes. Thus, the High-Energy Physics (HEP) field has benefited from advancements in information processing and the development of algorithms and tools for large datasets. More recently, quantum computing applications have been investigated in an effort to understand how the community can benefit from the advantages of quantum information science. In this manuscript, we provide an overview of the state-of-the-art applications of quantum computing to data analysis in HEP, discuss the challenges and opportunities in integrating these novel analysis techniques into a day-to-day analysis workflow, and whether there is potential for a quantum advantage.

Related papers

• The curse of random quantum data [62.24825255497622]
  We quantify the performances of quantum machine learning in the landscape of quantum data. We find that the training efficiency and generalization capabilities in quantum machine learning will be exponentially suppressed with the increase in qubits. Our findings apply to both the quantum kernel method and the large-width limit of quantum neural networks.
  arXiv  Detail & Related papers  (2024-08-19T12:18:07Z)
• Streaming IoT Data and the Quantum Edge: A Classic/Quantum Machine Learning Use Case [0.6554326244334868]
  We investigate the use of Edge computing for the integration of quantum machine learning into a distributed computing continuum. We present preliminary results for quantum machine learning analytics on an IoT scenario.
  arXiv  Detail & Related papers  (2024-02-23T10:36:22Z)
• Neural auto-designer for enhanced quantum kernels [59.616404192966016]
  We present a data-driven approach that automates the design of problem-specific quantum feature maps. Our work highlights the substantial role of deep learning in advancing quantum machine learning.
  arXiv  Detail & Related papers  (2024-01-20T03:11:59Z)
• Quantum-centric Supercomputing for Materials Science: A Perspective on Challenges and Future Directions [20.785521465797203]
  Hard computational tasks in materials science stretch the limits of existing high-performance supercomputing centers. Quantum computing, on the other hand, is an emerging technology with the potential to accelerate many of the computational tasks needed for materials science.
  arXiv  Detail & Related papers  (2023-12-14T18:14:22Z)
• Quantum Computing for High-Energy Physics: State of the Art and Challenges. Summary of the QC4HEP Working Group [33.8590861326926]
  This paper is led by CERN, DESY and IBM and provides the status of high-energy physics quantum computations. We 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.
  arXiv  Detail & Related papers  (2023-07-06T18:01:02Z)
• Quantum data learning for quantum simulations in high-energy physics [55.41644538483948]
  We explore the applicability of quantum-data learning to practical problems in high-energy physics. We make use of ansatz based on quantum convolutional neural networks and numerically show that it is capable of recognizing quantum phases of ground states. The observation of non-trivial learning properties demonstrated in these benchmarks will motivate further exploration of the quantum-data learning architecture in high-energy physics.
  arXiv  Detail & Related papers  (2023-06-29T18:00:01Z)
• Fitting a Collider in a Quantum Computer: Tackling the Challenges of Quantum Machine Learning for Big Datasets [0.0]
  Feature and data prototype selection techniques were studied to tackle this challenge. A grid search was performed and quantum machine learning models were trained and benchmarked against classical shallow machine learning methods. The performance of the quantum algorithms was found to be comparable to the classical ones, even when using large datasets.
  arXiv  Detail & Related papers  (2022-11-06T22:45:37Z)
• Snowmass Computational Frontier: Topical Group Report on Quantum Computing [0.8594140167290096]
  This report outlines how Quantum Information Science (QIS) and High Energy Physics (HEP) are deeply intertwined. Quantum computers do not represent a detour for HEP, rather they are set to become an integral part of our discovery toolkit. The role of quantum technologies across the entire economy is expected to grow rapidly over the next decade.
  arXiv  Detail & Related papers  (2022-09-14T17:10:20Z)
• Quantum-tailored machine-learning characterization of a superconducting qubit [50.591267188664666]
  We develop an approach to characterize the dynamics of a quantum device and learn device parameters. This approach outperforms physics-agnostic recurrent neural networks trained on numerically generated and experimental data. This demonstration shows how leveraging domain knowledge improves the accuracy and efficiency of this characterization task.
  arXiv  Detail & Related papers  (2021-06-24T15:58:57Z)
• Simulating Quantum Materials with Digital Quantum Computers [55.41644538483948]
  Digital quantum computers (DQCs) can efficiently perform quantum simulations that are otherwise intractable on classical computers. The aim of this review is to provide a summary of progress made towards achieving physical quantum advantage.
  arXiv  Detail & Related papers  (2021-01-21T20:10:38Z)
• Nearest Centroid Classification on a Trapped Ion Quantum Computer [57.5195654107363]
  We design a quantum Nearest Centroid classifier, using techniques for efficiently loading classical data into quantum states and performing distance estimations. We experimentally demonstrate it on a 11-qubit trapped-ion quantum machine, matching the accuracy of classical nearest centroid classifiers for the MNIST handwritten digits dataset and achieving up to 100% accuracy for 8-dimensional synthetic data.
  arXiv  Detail & Related papers  (2020-12-08T01:10:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.