Quantum Computing for High-Energy Physics: State of the Art and
Challenges. Summary of the QC4HEP Working Group
- URL: http://arxiv.org/abs/2307.03236v1
- Date: Thu, 6 Jul 2023 18:01:02 GMT
- Title: Quantum Computing for High-Energy Physics: State of the Art and
Challenges. Summary of the QC4HEP Working Group
- Authors: Alberto Di Meglio, Karl Jansen, Ivano Tavernelli, Constantia
Alexandrou, Srinivasan Arunachalam, Christian W. Bauer, Kerstin Borras,
Stefano Carrazza, Arianna Crippa, Vincent Croft, Roland de Putter, Andrea
Delgado, Vedran Dunjko, Daniel J. Egger, Elias Fernandez-Combarro, Elina
Fuchs, Lena Funcke, Daniel Gonzalez-Cuadra, Michele Grossi, Jad C. Halimeh,
Zoe Holmes, Stefan Kuhn, Denis Lacroix, Randy Lewis, Donatella Lucchesi,
Miriam Lucio Martinez, Federico Meloni, Antonio Mezzacapo, Simone Montangero,
Lento Nagano, Voica Radescu, Enrique Rico Ortega, Alessandro Roggero, Julian
Schuhmacher, Joao Seixas, Pietro Silvi, Panagiotis Spentzouris, Francesco
Tacchino, Kristan Temme, Koji Terashi, Jordi Tura, Cenk Tuysuz, Sofia
Vallecorsa, Uwe-Jens Wiese, Shinjae Yoo, Jinglei Zhang
- Abstract summary: 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.
- Score: 33.8590861326926
- License: http://creativecommons.org/licenses/by-sa/4.0/
- 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.
Related papers
- Assessing and Advancing the Potential of Quantum Computing: A NASA Case Study [11.29246196323319]
We describe NASA's work in assessing and advancing the potential of quantum computing.
We discuss advances in algorithms, both near- and longer-term, and the results of our explorations on current hardware and with simulations.
This work also includes physics-inspired classical algorithms that can be used at application scale today.
arXiv Detail & Related papers (2024-06-21T19:05:42Z) - Quantum algorithms for scientific applications [0.0]
Areas that are likely to have the greatest impact on high performance computing include simulation of quantum systems, optimisation, and machine learning.
Even a modest quantum enhancement to current classical techniques would have far-reaching impacts in areas such as weather forecasting, engineering, aerospace, drug design, and realising "green" materials for sustainable development.
arXiv Detail & Related papers (2023-12-22T18:29:31Z) - 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) - Quantum Machine Learning: from physics to software engineering [58.720142291102135]
We show how classical machine learning approach can help improve the facilities of quantum computers.
We discuss how quantum algorithms and quantum computers may be useful for solving classical machine learning tasks.
arXiv Detail & Related papers (2023-01-04T23:37:45Z) - Optimal Stochastic Resource Allocation for Distributed Quantum Computing [50.809738453571015]
We propose a resource allocation scheme for distributed quantum computing (DQC) based on programming to minimize the total deployment cost for quantum resources.
The evaluation demonstrates the effectiveness and ability of the proposed scheme to balance the utilization of quantum computers and on-demand quantum computers.
arXiv Detail & Related papers (2022-09-16T02:37:32Z) - Recent Advances for Quantum Neural Networks in Generative Learning [98.88205308106778]
Quantum generative learning models (QGLMs) may surpass their classical counterparts.
We review the current progress of QGLMs from the perspective of machine learning.
We discuss the potential applications of QGLMs in both conventional machine learning tasks and quantum physics.
arXiv Detail & Related papers (2022-06-07T07:32:57Z) - Evolution of Quantum Computing: A Systematic Survey on the Use of
Quantum Computing Tools [5.557009030881896]
We conduct a systematic survey and categorize papers, tools, frameworks, platforms that facilitate quantum computing.
We discuss the current essence, identify open challenges and provide future research direction.
We conclude that scores of frameworks, tools and platforms are emerged in the past few years, improvement of currently available facilities would exploit the research activities in the quantum research community.
arXiv Detail & Related papers (2022-04-04T21:21:12Z) - Quantum computing for data analysis in high energy physics [1.6002009406818865]
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.
arXiv Detail & Related papers (2022-03-15T18:27:43Z) - 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) - Quantum Computation [0.0]
We will discuss and summarized the core principles and practical application areas of quantum computation.
The mapping of computation onto the behavior of physical systems is a historical challenge.
We will evaluate the essential technology required for quantum computers to be able to function correctly.
arXiv Detail & Related papers (2020-06-04T11:57:18Z) - An Application of Quantum Annealing Computing to Seismic Inversion [55.41644538483948]
We apply a quantum algorithm to a D-Wave quantum annealer to solve a small scale seismic inversions problem.
The accuracy achieved by the quantum computer is at least as good as that of the classical computer.
arXiv Detail & Related papers (2020-05-06T14:18:44Z)
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.