Quantum Computing and Visualization: A Disruptive Technological Change
Ahead
- URL: http://arxiv.org/abs/2310.04937v2
- Date: Wed, 11 Oct 2023 04:29:27 GMT
- Title: Quantum Computing and Visualization: A Disruptive Technological Change
Ahead
- Authors: E. Wes Bethel and Mercy G. Amankwah and Jan Balewski and Roel Van
Beeumen and Daan Camps and Daniel Huang and Talita Perciano
- Abstract summary: The focus of this article is to explore ideas related to how visualization helps in understanding Quantum Computing (QC)
QC is emerging as a promising pathway to overcome the growth limits in classical computing.
visualization has played a role in QC by providing the means to show representations of the quantum state of single-qubits in superposition states and multiple-qubits in entangled states.
- Score: 0.753179862869346
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The focus of this Visualization Viewpoints article is to provide some
background on Quantum Computing (QC), to explore ideas related to how
visualization helps in understanding QC, and examine how QC might be useful for
visualization with the growth and maturation of both technologies in the
future. In a quickly evolving technology landscape, QC is emerging as a
promising pathway to overcome the growth limits in classical computing. In some
cases, QC platforms offer the potential to vastly outperform the familiar
classical computer by solving problems more quickly or that may be intractable
on any known classical platform. As further performance gains for classical
computing platforms are limited by diminishing Moore's Law scaling, QC
platforms might be viewed as a potential successor to the current field of
exascale-class platforms. While present-day QC hardware platforms are still
limited in scale, the field of quantum computing is robust and rapidly
advancing in terms of hardware capabilities, software environments for
developing quantum algorithms, and educational programs for training the next
generation of scientists and engineers. After a brief introduction to QC
concepts, the focus of this article is to explore the interplay between the
fields of visualization and QC. First, visualization has played a role in QC by
providing the means to show representations of the quantum state of
single-qubits in superposition states and multiple-qubits in entangled states.
Second, there are a number of ways in which the field of visual data
exploration and analysis may potentially benefit from this disruptive new
technology though there are challenges going forward.
Related papers
- Integrating Quantum Computing Resources into Scientific HPC Ecosystems [29.1407119677928]
Quantum Computing offers significant potential to enhance scientific discovery in fields such as quantum chemistry, optimization, and artificial intelligence.
QC faces challenges due to the noisy intermediate-scale quantum era's inherent external noise issues.
This paper outlines plans to unlock new computational possibilities, driving forward scientific inquiry and innovation in a wide array of research domains.
arXiv Detail & Related papers (2024-08-28T22:44:54Z) - Technology and Performance Benchmarks of IQM's 20-Qubit Quantum Computer [56.435136806763055]
IQM Quantum Computers is described covering both the QPU and the rest of the full-stack quantum computer.
The focus is on a 20-qubit quantum computer featuring the Garnet QPU and its architecture, which we will scale up to 150 qubits.
We present QPU and system-level benchmarks, including a median 2-qubit gate fidelity of 99.5% and genuinely entangling all 20 qubits in a Greenberger-Horne-Zeilinger (GHZ) state.
arXiv Detail & Related papers (2024-08-22T14:26:10Z) - Rethinking Programming Paradigms in the QC-HPC Context [1.1132768046061499]
We explore avenues of refinement for the quantum processing unit (QPU) in the context of many-tasks management.
We illustrate how its potential for scientific discovery might be realized.
arXiv Detail & Related papers (2024-06-05T14:44:19Z) - QCSHQD: Quantum computing as a service for Hybrid classical-quantum software development: A Vision [4.6103649840975365]
This study presents a blueprint for QCSHQD, designed to democratize access to QC resources for classical developers.
The vision of QCSHQD paves the way for groundbreaking innovations by addressing key challenges of hybridization between classical and quantum computers.
arXiv Detail & Related papers (2024-03-13T16:16:43Z) - Towards Quantum-Native Communication Systems: New Developments, Trends,
and Challenges [63.67245855948243]
The survey examines technologies such as quantum-domain (QD) multi-input multi-output (MIMO), QD non-orthogonal multiple access (NOMA), quantum secure direct communication (QSDC)
The current status of quantum sensing, quantum radar, and quantum timing is briefly reviewed in support of future applications.
arXiv Detail & Related papers (2023-11-09T09:45:52Z) - Integration of Quantum Accelerators with High Performance Computing -- A
Review of Quantum Programming Tools [0.8477185635891722]
This study aims to characterize existing quantum programming tools (QPTs) from an HPC perspective.
It investigates if existing QPTs have the potential to be efficiently integrated with classical computing models.
This work structures a set of criteria into an analysis blueprint that enables HPC scientists to assess whether a QPT is suitable for the quantum-accelerated classical application.
arXiv Detail & Related papers (2023-09-12T12:24:12Z) - 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) - 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) - Physics-Informed Quantum Communication Networks: A Vision Towards the
Quantum Internet [79.8886946157912]
We present a novel analysis of the performance of quantum communication networks (QCNs) in a physics-informed manner.
The need of the physics-informed approach is then assessed and its fundamental role in designing practical QCNs is analyzed.
We identify novel physics-informed performance metrics and controls that enable QCNs to leverage the state-of-the-art advancements in quantum technologies.
arXiv Detail & Related papers (2022-04-20T05:32:16Z) - 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) - 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)
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.