A Gateway to Quantum Computing for Industrial Engineering
- URL: http://arxiv.org/abs/2510.20620v1
- Date: Thu, 23 Oct 2025 14:54:11 GMT
- Title: A Gateway to Quantum Computing for Industrial Engineering
- Authors: Emily L. Tucker, Mohammadhossein Mohammadisiahroudi,
- Abstract summary: We provide a road map of the current field of quantum operations research.<n>We introduce the foundational principles of quantum computing, outline the current hardware and software landscape.<n>We highlight research directions, including the importance of problem domains for driving long-term value of quantum computers.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Quantum computing is rapidly emerging as a new computing paradigm with the potential to improve decision-making, optimization, and simulation across industries. For industrial engineering (IE) and operations research (OR), this shift introduces both unprecedented opportunities and substantial challenges. The learning curve is high, and to help researchers navigate the emerging field of quantum operations research, we provide a road map of the current field of quantum operations research. We introduce the foundational principles of quantum computing, outline the current hardware and software landscape, and survey major algorithmic advances relevant to IE/OR, including quantum approaches to linear algebra, optimization, machine learning, and stochastic simulation. We then highlight applied research directions, including the importance of problem domains for driving long-term value of quantum computers and how existing classical OR models can be reformulated for quantum hardware. Recognizing the steep learning curve, we propose pathways for IE/OR researchers to develop technical fluency and engage in this interdisciplinary domain. By bridging theory with application, and emphasizing the interplay between hardware and research development, we argue that industrial engineers are uniquely positioned to shape the trajectory of quantum computing for practical problem-solving. Ultimately, we aim to lower the barrier to entry into quantum computing, motivate new collaborations, and chart future directions where quantum technologies may deliver tangible impact for industry and academia.
Related papers
- Quantum-enhanced Computer Vision: Going Beyond Classical Algorithms [50.573955644831386]
Quantum-enhanced Computer Vision (QeCV) is a new research field at the intersection of computer vision, machine learning and quantum computing.<n>It has high potential to transform how visual signals are processed and interpreted with the help of quantum computing.<n>This survey contributes to the existing literature on QeCV with a holistic review of this research field.
arXiv Detail & Related papers (2025-10-08T17:59:51Z) - Quantum-Based Software Engineering [2.0203155038047127]
We introduce Quantum-Based Software Engineering (QBSE) as a new research direction for applying quantum computing to software engineering problems.<n>We outline its scope, clarify its distinction from quantum software engineering (QSE), and identify key problem types that may benefit from quantum optimization, search, and learning techniques.
arXiv Detail & Related papers (2025-05-29T17:19:38Z) - A Survey of Quantum Transformers: Architectures, Challenges and Outlooks [82.4736481748099]
Quantum Transformers integrate the representational power of classical Transformers with the computational advantages of quantum computing.<n>Since 2022, research in this area has rapidly expanded, giving rise to diverse technical paradigms and early applications.<n>This paper presents the first comprehensive, systematic, and in-depth survey of quantum Transformer models.
arXiv Detail & Related papers (2025-04-04T05:40:18Z) - Quantum Machine Learning: An Interplay Between Quantum Computing and Machine Learning [54.80832749095356]
Quantum machine learning (QML) is a rapidly growing field that combines quantum computing principles with traditional machine learning.
This paper introduces quantum computing for the machine learning paradigm, where variational quantum circuits are used to develop QML architectures.
arXiv Detail & Related papers (2024-11-14T12:27:50Z) - A Review of Quantum Scientific Computing Algorithms for Engineering Problems [0.0]
Quantum computing, leveraging quantum phenomena like superposition and entanglement, is emerging as a transformative force in computing technology.
This paper systematically explores the foundational concepts of quantum mechanics and their implications for computational advancements.
arXiv Detail & Related papers (2024-08-25T21:40:22Z) - Quantum Circuit Synthesis and Compilation Optimization: Overview and Prospects [59.07692103357675]
This survey explores the feasibility of an integrated design and optimization scheme that spans from the algorithmic level to quantum hardware.<n>It becomes more possible to reduce manual design costs, enhance the precision and efficiency of execution, and facilitate the implementation and validation of the superiority of quantum algorithms on hardware.
arXiv Detail & Related papers (2024-06-30T15:50:10Z) - Quantum algorithms for scientific computing [0.0]
Areas that are likely to have the greatest impact on high performance computing include simulation of quantum systems, optimization, and machine learning.<n>Even a modest quantum enhancement to current classical techniques would have far-reaching impacts in areas such as weather forecasting, aerospace engineering, and the design of "green" materials for sustainable development.
arXiv Detail & Related papers (2023-12-22T18:29:31Z) - The QUATRO Application Suite: Quantum Computing for Models of Human
Cognition [49.038807589598285]
We unlock a new class of applications ripe for quantum computing research -- computational cognitive modeling.
We release QUATRO, a collection of quantum computing applications from cognitive models.
arXiv Detail & Related papers (2023-09-01T17:34:53Z) - Quantum Software Engineering Challenges from Developers' Perspective:
Mapping Research Challenges to the Proposed Workflow Model [5.287156503763459]
Software engineering of quantum programs can be approached from two directions.
In this paper, we aim at bridging the gap by starting with the quantum computing workflow and by mapping existing software engineering research to this workflow.
arXiv Detail & Related papers (2023-08-02T13:32:31Z) - 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) - Multi-disk clutch optimization using quantum annealing [34.82692226532414]
We develop a new quantum algorithm to solve a problem with significant practical relevance in clutch manufacturing.
It is demonstrated how quantum optimization can play a role in real industrial applications in the manufacturing sector.
arXiv Detail & Related papers (2022-08-11T16:34:51Z) - 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) - 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.