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Mathematics
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Quantum Computing Algorithms and Quantum Computing Simulators
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Quantum Computing Algorithms and Quantum Computing Simulators

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

Deadline for manuscript submissions: 31 December 2024 | Viewed by 6252

Special Issue Editors

Dr. Fernando L. Pelayo

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Guest Editor
Escuela Superior de Ingenieria Informatica de Albacete, Computing Systems Department, University of Castilla-La Mancha, 02071 Albacete, Spain
Interests: quantum computing; algorithms complexity; formal models of concurrency; discrete dynamical systems
Special Issues, Collections and Topics in MDPI journals
Dr. Mauro Mezzini

E-Mail Website
Guest Editor
Department of Education, Roma Tre University, 00154 Roma, Italy
Interests: neural networks; graph and hypergraph algorithms; quantum algorithms
Special Issues, Collections and Topics in MDPI journals
Dr. Pedro Valero-Lara

E-Mail Website
Guest Editor
Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
Interests: high performance computing; parallel programming; linear algebra; computational fluid dynamics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Quantum computing is a hot field of research at the intersection of mathematics, computer science, and physics that promises to significantly revolutionize many technological aspects associated with medicine, machine learning, artificial intelligence, cryptography, and operations research, among others.

Investors and governments from all over the world promote its development, assuming beyond any doubt its strategic importance. In this sense, they dedicate a lot of resources to developing quantum computing in countries such as China, India, the United States, Canada, etc…

Nevertheless, its level of development does not correspond to that of a mature discipline, especially at the hardware level where the decoherence quantitatively hinders the implementation of universal quantum computing without restrictions.

The above reason reinforces the importance of quantum computing simulation as a platform for learning, training, and testing quantum algorithms, in this meanwhile. Quantum computing simulators necessarily involve aspects of high-performance computing, as well as applied mathematics.

We dedicate this Special Issue to quantum computing and some related aspects including:

  • Quantum algorithms
  • Quantum computing
  • Computational complexity
  • Quantum computing simulation.

Dr. Fernando L. Pelayo
Dr. Mauro Mezzini
Dr. Pedro Valero-Lara
Guest Editors

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.

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Published Papers (3 papers)

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Research

12 pages, 1821 KiB  
Article
Quantum Machine Learning for Credit Scoring
by Nikolaos Schetakis, Davit Aghamalyan, Michael Boguslavsky, Agnieszka Rees, Marc Rakotomalala and Paul Robert Griffin
Mathematics 2024, 12(9), 1391; https://doi.org/10.3390/math12091391 - 2 May 2024
Viewed by 2009
Abstract
This study investigates the integration of quantum circuits with classical neural networks for enhancing credit scoring for small- and medium-sized enterprises (SMEs). We introduce a hybrid quantum–classical model, focusing on the synergy between quantum and classical rather than comparing the performance of separate [...] Read more.
This study investigates the integration of quantum circuits with classical neural networks for enhancing credit scoring for small- and medium-sized enterprises (SMEs). We introduce a hybrid quantum–classical model, focusing on the synergy between quantum and classical rather than comparing the performance of separate quantum and classical models. Our model incorporates a quantum layer into a traditional neural network, achieving notable reductions in training time. We apply this innovative framework to a binary classification task with a proprietary real-world classical credit default dataset for SMEs in Singapore. The results indicate that our hybrid model achieves efficient training, requiring significantly fewer epochs (350) compared to its classical counterpart (3500) for a similar predictive accuracy. However, we observed a decrease in performance when expanding the model beyond 12 qubits or when adding additional quantum classifier blocks. This paper also considers practical challenges faced when deploying such models on quantum simulators and actual quantum computers. Overall, our quantum–classical hybrid model for credit scoring reveals its potential in industry, despite encountering certain scalability limitations and practical challenges. Full article
(This article belongs to the Special Issue Quantum Computing Algorithms and Quantum Computing Simulators)
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20 pages, 1124 KiB  
Article
Functional Matrices on Quantum Computing Simulation
by Hernán Indíbil de la Cruz Calvo, Fernando Cuartero Gómez, José Javier Paulet González, Mauro Mezzini and Fernando López Pelayo
Mathematics 2023, 11(17), 3742; https://doi.org/10.3390/math11173742 - 31 Aug 2023
Viewed by 1600
Abstract
In simulating Quantum Computing by using the circuit model the size of the matrices to deal with, together with the number of products and additions required to apply every quantum gate becomes a really hard computational restriction. This paper presents a data structure, [...] Read more.
In simulating Quantum Computing by using the circuit model the size of the matrices to deal with, together with the number of products and additions required to apply every quantum gate becomes a really hard computational restriction. This paper presents a data structure, called Functional Matrices, which is the most representative feature of QSimov quantum computing simulator which is also provided and tested. A comparative study of the performance of Functional Matrices with respect to the other two most commonly used matrix data structures, dense and sparse ones, is also performed and summarized within this work. Full article
(This article belongs to the Special Issue Quantum Computing Algorithms and Quantum Computing Simulators)
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24 pages, 1062 KiB  
Article
Heuristics for Quantum Computing Dealing with 3-SAT
by Jose J. Paulet, Luis F. LLana, Hernán Indíbil Calvo, Mauro Mezzini, Fernando Cuartero and Fernando L. Pelayo
Mathematics 2023, 11(8), 1888; https://doi.org/10.3390/math11081888 - 16 Apr 2023
Viewed by 1943
Abstract
The SAT problem is maybe one of the most famous NP-complete problems. This paper deals with the 3-SAT problem. We follow a sort of incremental strategy to save computational costs with respect to the classical quantum computing approach. We present an heuristics that [...] Read more.
The SAT problem is maybe one of the most famous NP-complete problems. This paper deals with the 3-SAT problem. We follow a sort of incremental strategy to save computational costs with respect to the classical quantum computing approach. We present an heuristics that leads this strategy, improving the performance of the purely random incremental scheme. We finally validate our approach by means of a thorough empirical study. Full article
(This article belongs to the Special Issue Quantum Computing Algorithms and Quantum Computing Simulators)
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