Quantum Frontiers in High Energy Physics
- URL: http://arxiv.org/abs/2411.11294v1
- Date: Mon, 18 Nov 2024 05:41:08 GMT
- Title: Quantum Frontiers in High Energy Physics
- Authors: Yaquan Fang, Christina Gao, Ying-Ying Li, Jing Shu, Yusheng Wu, Hongxi Xing, Bin Xu, Lailin Xu, Chen Zhou,
- Abstract summary: We will discuss the potential of quantum devices in detecting subtle effects indicative of new physics beyond the Standard Model.
We will also discuss the transformative role of quantum algorithms and large-scale quantum computers in studying real-time non-perturbative dynamics in the early universe and at colliders.
- Score: 9.663373038813354
- License:
- Abstract: Numerous challenges persist in High Energy Physics (HEP), the addressing of which requires advancements in detection technology, computational methods, data analysis frameworks, and phenomenological designs. We provide a concise yet comprehensive overview of recent progress across these areas, in line with advances in quantum technology. We will discuss the potential of quantum devices in detecting subtle effects indicative of new physics beyond the Standard Model, the transformative role of quantum algorithms and large-scale quantum computers in studying real-time non-perturbative dynamics in the early universe and at colliders, as well as in analyzing complex HEP data. Additionally, we emphasize the importance of integrating quantum properties into HEP experiments to test quantum mechanics at unprecedented high-energy scales and search for hints of new physics. Looking ahead, the continued integration of resources to fully harness these evolving technologies will enhance our efforts to deepen our understanding of the fundamental laws of nature.
Related papers
- Information scrambling -- a quantum thermodynamic perspective [0.0]
Recent advances in quantum information science have shed light on the intricate dynamics of quantum many-body systems.
This perspective aims at synthesizing key findings from several pivotal studies and exploring various aspects of quantum scrambling.
arXiv Detail & Related papers (2024-01-10T18:15:09Z) - Entanglement-Assisted Quantum Networks: Mechanics, Enabling
Technologies, Challenges, and Research Directions [66.27337498864556]
This paper presents a comprehensive survey of entanglement-assisted quantum networks.
It provides a detailed overview of the network structure, working principles, and development stages.
It also emphasizes open research directions, including architecture design, entanglement-based network issues, and standardization.
arXiv Detail & Related papers (2023-07-24T02:48:22Z) - 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) - 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) - 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) - 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) - Materials and devices for fundamental quantum science and quantum
technologies [41.6785981575436]
We focus on advanced superconducting materials, van der Waals materials, and moir'e quantum matter.
We highlight a wealth of potential applications, ranging from high-energy experimental and theoretical physics to quantum materials science and energy storage.
arXiv Detail & Related papers (2022-01-23T13:33:19Z) - Standard Model Physics and the Digital Quantum Revolution: Thoughts
about the Interface [68.8204255655161]
Advances in isolating, controlling and entangling quantum systems are transforming what was once a curious feature of quantum mechanics into a vehicle for disruptive scientific and technological progress.
From the perspective of three domain science theorists, this article compiles thoughts about the interface on entanglement, complexity, and quantum simulation.
arXiv Detail & Related papers (2021-07-10T06:12:06Z) - 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) - Machine Learning for Quantum Matter [0.0]
We review the recent development and adaptation of machine learning ideas for the purpose advancing research in quantum matter.
We discuss the outlook for future developments in areas at the intersection between machine learning and quantum many-body physics.
arXiv Detail & Related papers (2020-03-24T18:00:30Z)
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.