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Mathematics
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Quantum Computing for Industrial Applications
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Quantum Computing for Industrial Applications

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".

Deadline for manuscript submissions: 25 December 2024 | Viewed by 13903

Special Issue Editor

Prof. Dr. Jose L. Salmeron

E-Mail Website
Guest Editor
Data Science Laboratory, Universidad Pablo de Olavide, Seville, Spain
Interests: data science; computational intelligence; soft computing; quantum computing; explainable artificial intelligence; distributed artificial intelligence; machine learning

Special Issue Information

Dear Colleagues,

Quantum computing is a promising field for real-world industrial applications. Quantum computing applies the principles of quantum mechanics to process information. It would be a step-forward, especially where classical approaches suffer from excessive time and/or space complexity. In this sense, quantum machine learning explores how to design and implement quantum software that could enable machine learning that is faster or even more accurate.  Quantum machine learning fills the gaps between the theoretical advances in quantum computing and the machine learning field. The current state of quantum machine learning could be compared to conventional computing in the 1950s. According to this, the forthcoming roadmap is full of challenges and exciting discoveries.

This Special Issue of Mathematics welcomes academic and industrial research on quantum computing. Topics include, but are not limited to:

  • Practical implementations of quantum machine learning;
  • Quantum machine learning algorithms;
  • Quantum programming languages;
  • Distributed quantum machine learning;
  • Quantum machine learning in drug discovery;
  • Quantum machine learning for classification;
  • Quantum machine learning for regression;
  • Quantum machine learning for reinforcement learning;
  • Quantum computing for simulation purposes. 

Prof. Dr. Jose Salmeron
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Quantum Computing
  • Quantum Machine Learning
  • Quantum optimisation
  • Quantum Programming Languages
  • Industrial Applications of Quantum Computing
  • Quantum computing in Healthcare
  • Quantum Algorithms
  • Complexity

Published Papers (6 papers)

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Research

19 pages, 1333 KiB  
Article
VaR Estimation with Quantum Computing Noise Correction Using Neural Networks
by Luis de Pedro, Raúl París Murillo, Jorge E. López de Vergara, Sergio López-Buedo and Francisco J. Gómez-Arribas
Mathematics 2023, 11(20), 4355; https://0-doi-org.brum.beds.ac.uk/10.3390/math11204355 - 20 Oct 2023
Viewed by 848
Abstract
In this paper, we present the development of a quantum computing method for calculating the value at risk (VaR) for a portfolio of assets managed by a finance institution. We extend the conventional Monte Carlo algorithm to calculate the [...] Read more.
In this paper, we present the development of a quantum computing method for calculating the value at risk (VaR) for a portfolio of assets managed by a finance institution. We extend the conventional Monte Carlo algorithm to calculate the VaR of an arbitrary number of assets by employing random variable algebra and Taylor series approximation. The resulting algorithm is suitable to be executed in real quantum computers. However, the noise affecting current quantum computers renders them almost useless for the task. We present a methodology to mitigate the noise impact by using neural networks to compensate for the noise effects. The system combines the output from a real quantum computer with the neural network processing. The feedback is used to fine tune the quantum circuits. The results show that this approach is useful for estimating the VaR in finance institutions, particularly when dealing with a large number of assets. We demonstrate the validity of the proposed method with up to 139 assets. The accuracy of the method is also proven. We achieved an error of less than 1% in the empirical measurements with respect to the parametric model. Full article
(This article belongs to the Special Issue Quantum Computing for Industrial Applications)
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14 pages, 1668 KiB  
Article
Variational Quantum Circuit Topology Grid Search for Hypocalcemia Following Thyroid Surgery
by Jose L. Salmeron and Isabel Fernández-Palop
Mathematics 2023, 11(17), 3659; https://0-doi-org.brum.beds.ac.uk/10.3390/math11173659 - 24 Aug 2023
Viewed by 1133
Abstract
Quantum computing’s potential to revolutionise medical applications has spurred interest in leveraging quantum algorithms for healthcare challenges. In this research, the authors explored the application of variational quantum circuits to predicting hypocalcemia risk following thyroid surgery. Hypocalcemia, resulting from hypoparathyroidism, is a common [...] Read more.
Quantum computing’s potential to revolutionise medical applications has spurred interest in leveraging quantum algorithms for healthcare challenges. In this research, the authors explored the application of variational quantum circuits to predicting hypocalcemia risk following thyroid surgery. Hypocalcemia, resulting from hypoparathyroidism, is a common post-surgical complication. This novel approach includes a topology grid search of the variational quantum circuits. To execute the grid search, our research employed a classical optimiser that guided the adjustment of different circuit topologies, assessing their impact on predictive performance. Our research used, as relevant features, an intra-operative PTH (parathyroid hormone) at 10 min post-removal and percentage decrease of pre-operative and intra-operative PTH levels. The findings revealed insights into the interplay between variational quantum circuit topologies and predictive accuracy for hypocalcemia risk assessment. Full article
(This article belongs to the Special Issue Quantum Computing for Industrial Applications)
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21 pages, 4928 KiB  
Article
Quantum Computing Meets Deep Learning: A Promising Approach for Diabetic Retinopathy Classification
by Shtwai Alsubai, Abdullah Alqahtani, Adel Binbusayyis, Mohemmed Sha, Abdu Gumaei and Shuihua Wang
Mathematics 2023, 11(9), 2008; https://0-doi-org.brum.beds.ac.uk/10.3390/math11092008 - 24 Apr 2023
Cited by 5 | Viewed by 2142
Abstract
Diabetic retinopathy seems to be the cause of micro-vascular retinal alterations. It remains a leading reason for blindness and vision loss in adults around the age of 20 to 74. Screening for this disease has become vital in identifying referable cases that require [...] Read more.
Diabetic retinopathy seems to be the cause of micro-vascular retinal alterations. It remains a leading reason for blindness and vision loss in adults around the age of 20 to 74. Screening for this disease has become vital in identifying referable cases that require complete ophthalmic evaluation and treatment to avoid permanent loss of vision. The computer-aided design could ease this screening process, which requires limited time, and assist clinicians. The main complexity in classifying images involves huge computation, leading to slow classification. Certain image classification approaches integrating quantum computing have recently evolved to resolve this. With its parallel computing ability, quantum computing could assist in effective classification. The notion of integrating quantum computing with conventional image classification methods is theoretically feasible and advantageous. However, as existing image classification techniques have failed to procure high accuracy in classification, a robust approach is needed. The present research proposes a quantum-based deep convolutional neural network to avert these pitfalls and identify disease grades from the Indian Diabetic Retinopathy Image Dataset. Typically, quantum computing could make use of the maximum number of entangled qubits for image reconstruction without any additional information. This study involves conceptual enhancement by proposing an optimized structural system termed an optimized multiple-qbit gate quantum neural network for the classification of DR. In this case, multiple qubits are regarded as the ability of qubits in multiple states to exist concurrently, which permits performance improvement with the distinct additional qubit. The overall performance of this system is validated in accordance with performance metrics, and the proposed method achieves 100% accuracy, 100% precision, 100% recall, 100% specificity, and 100% f1-score. Full article
(This article belongs to the Special Issue Quantum Computing for Industrial Applications)
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13 pages, 2127 KiB  
Article
Quantum Computing in Insurance Capital Modelling
by Muhsin Tamturk
Mathematics 2023, 11(3), 658; https://0-doi-org.brum.beds.ac.uk/10.3390/math11030658 - 28 Jan 2023
Cited by 1 | Viewed by 2403
Abstract
This paper proposes a quantum computing approach for insurance capital modelling. Using an open-source software development kit, Qiskit, an algorithm for working on a superconducting type IBM quantum computer is developed and implemented to predict the capital of insurance companies in the classical [...] Read more.
This paper proposes a quantum computing approach for insurance capital modelling. Using an open-source software development kit, Qiskit, an algorithm for working on a superconducting type IBM quantum computer is developed and implemented to predict the capital of insurance companies in the classical surplus process. With the fundamental properties of quantum mechanics, Dirac notation and Feynman’s path calculation are shown. Furthermore, custom quantum insurance premium and claim gates are investigated in order to build a quantum circuit with respect to initial reserve, premium and claim amounts. Some numerical results are presented and discussed at the end of the paper. Full article
(This article belongs to the Special Issue Quantum Computing for Industrial Applications)
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18 pages, 380 KiB  
Article
A Modular Framework for Generic Quantum Algorithms
by Alberto Manzano, Daniele Musso, Álvaro Leitao, Andrés Gómez, Carlos Vázquez, Gustavo Ordóñez and María R. Nogueiras
Mathematics 2022, 10(5), 785; https://0-doi-org.brum.beds.ac.uk/10.3390/math10050785 - 01 Mar 2022
Cited by 2 | Viewed by 2598
Abstract
We describe a general-purpose framework to design quantum algorithms. This framework relies on two pillars: a basic data structure called quantum matrix and a modular structure based on three quasi-independent modules. These latter include a loading module, a tool-kit of basic quantum arithmetic [...] Read more.
We describe a general-purpose framework to design quantum algorithms. This framework relies on two pillars: a basic data structure called quantum matrix and a modular structure based on three quasi-independent modules. These latter include a loading module, a tool-kit of basic quantum arithmetic operations and a read-out module. We briefly discuss the loading and read-out modules, while the arithmetic module is analyzed in more depth. Eventually, we give explicit examples regarding the manipulation of generic oracles and hint at possible applications. Full article
(This article belongs to the Special Issue Quantum Computing for Industrial Applications)
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19 pages, 922 KiB  
Article
Quantum-Inspired Interpretable AI-Empowered Decision Support System for Detection of Early-Stage Rheumatoid Arthritis in Primary Care Using Scarce Dataset
by Samira Abbasgholizadeh Rahimi, Mojtaba Kolahdoozi, Arka Mitra, Jose L. Salmeron, Amir Mohammad Navali, Alireza Sadeghpour and Seyed Amir Mir Mohammadi
Mathematics 2022, 10(3), 496; https://0-doi-org.brum.beds.ac.uk/10.3390/math10030496 - 03 Feb 2022
Cited by 4 | Viewed by 3250
Abstract
Rheumatoid arthritis (RA) is a chronic inflammatory and long-term autoimmune disease that can lead to joint and bone erosion. This can lead to patients’ disability if not treated in a timely manner. Early detection of RA in settings such as primary care (as [...] Read more.
Rheumatoid arthritis (RA) is a chronic inflammatory and long-term autoimmune disease that can lead to joint and bone erosion. This can lead to patients’ disability if not treated in a timely manner. Early detection of RA in settings such as primary care (as the first contact with patients) can have an important role on the timely treatment of the disease. We aim to develop a web-based Decision Support System (DSS) to provide a proper assistance for primary care providers in early detection of RA patients. Using Sparse Fuzzy Cognitive Maps, as well as quantum-learning algorithm, we developed an online web-based DSS to assist in early detection of RA patients, and subsequently classify the disease severity into six different levels. The development process was completed in collaborating with two specialists in orthopedic as well as rheumatology orthopedic surgery. We used a sample of anonymous patient data for development of our model which was collected from Shohada University Hospital, Tabriz, Iran. We compared the results of our model with other machine learning methods (e.g., linear discriminant analysis, Support Vector Machines, and K-Nearest Neighbors). In addition to outperforming other methods of machine learning in terms of accuracy when all of the clinical features are used (accuracy of 69.23%), our model identified the relation of the different features with each other and gave higher explainability comparing to the other methods. For future works, we suggest applying the proposed model in different contexts and comparing the results, as well as assessing its usefulness in clinical practice. Full article
(This article belongs to the Special Issue Quantum Computing for Industrial Applications)
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