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Quantum Software Engineering and Programming
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Quantum Software Engineering and Programming

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Quantum Science and Technology".

Deadline for manuscript submissions: closed (31 May 2022) | Viewed by 14078

Special Issue Editor

Prof. Dr. Mario Piattini

E-Mail Website
Guest Editor
Alarcos Research Group. University of Castilla-La Mancha, Paseo de la Universidad 4, 13071 Ciudad Real, Spain
Interests: quantum computing; quantum software engineering; information systems quality

Special Issue Information

Dear Colleagues,

Quantum computing is more and more becoming a mature field, having a great deal of direct applications in supply chain and logistics, chemistry, economics and financial services, energy and agriculture, medicine and health, etc. In the coming years, companies will progressively need to incorporate quantum software, and both the research and the practitioner communities must provide an answer to deal with this new challenge.

With the rise of the first quantum computers, several programming languages and quantum algorithms came up with promising results. Nevertheless, quantum software is not yet produced in a rigorous and industrial way. Software engineering and programming practices need to be brought into the domain of quantum computing.

Therefore, we hope that this Special Issue will provide an overall picture of the problems and challenges of developing quantum software and up-to-date software engineering processes, methods, techniques, practices, and principles for the development of quantum software to both researchers and practitioners.

Scope: Potential papers dealing with, but not limited to, the following topics are deemed suitable for publication:

  • Quantum software development problems;
  • Quantum programming challenges;
  • Quantum software testing;
  • Quantum programming development environments;
  • Quantum software patterns and best practices;
  • Quantum software quality;
  • Quantum services delivery and management;
  • Quantum software engineers’ education and training.

Prof. Dr. Mario Piattini
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. Applied Sciences 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 2400 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.

Published Papers (4 papers)

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Research

20 pages, 820 KiB  
Article
Quantum Compressive Sensing: Mathematical Machinery, Quantum Algorithms, and Quantum Circuitry
by Kyle M. Sherbert, Naveed Naimipour, Haleh Safavi, Harry C. Shaw and Mojtaba Soltanalian
Appl. Sci. 2022, 12(15), 7525; https://doi.org/10.3390/app12157525 - 26 Jul 2022
Cited by 1 | Viewed by 1608
Abstract
Compressive sensing is a sensing protocol that facilitates the reconstruction of large signals from relatively few measurements by exploiting known structures of signals of interest, typically manifested as signal sparsity. Compressive sensing’s vast repertoire of applications in areas such as communications and image [...] Read more.
Compressive sensing is a sensing protocol that facilitates the reconstruction of large signals from relatively few measurements by exploiting known structures of signals of interest, typically manifested as signal sparsity. Compressive sensing’s vast repertoire of applications in areas such as communications and image reconstruction stems from the traditional approach of utilizing non-linear optimization to exploit the sparsity assumption by selecting the lowest-weight (i.e., maximum sparsity) signal consistent with all acquired measurements. Recent efforts in the literature consider instead a data-driven approach, training tensor networks to learn the structure of signals of interest. The trained tensor network is updated to “project” its state onto one consistent with the measurements taken, and is then sampled site by site to “guess” the original signal. In this paper, we take advantage of this computing protocol by formulating an alternative “quantum” protocol, in which the state of the tensor network is a quantum state over a set of entangled qubits. Accordingly, we present the associated algorithms and quantum circuits required to implement the training, projection, and sampling steps on a quantum computer. We supplement our theoretical results by simulating the proposed circuits with a small, qualitative model of LIDAR imaging of earth forests. Our results indicate that a quantum, data-driven approach to compressive sensing may have significant promise as quantum technology continues to make new leaps. Full article
(This article belongs to the Special Issue Quantum Software Engineering and Programming)
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20 pages, 653 KiB  
Article
Efficient Decomposition of Unitary Matrices in Quantum Circuit Compilers
by Anna M. Krol, Aritra Sarkar, Imran Ashraf, Zaid Al-Ars and Koen Bertels
Appl. Sci. 2022, 12(2), 759; https://doi.org/10.3390/app12020759 - 12 Jan 2022
Cited by 15 | Viewed by 3997
Abstract
Unitary decomposition is a widely used method to map quantum algorithms to an arbitrary set of quantum gates. Efficient implementation of this decomposition allows for the translation of bigger unitary gates into elementary quantum operations, which is key to executing these algorithms on [...] Read more.
Unitary decomposition is a widely used method to map quantum algorithms to an arbitrary set of quantum gates. Efficient implementation of this decomposition allows for the translation of bigger unitary gates into elementary quantum operations, which is key to executing these algorithms on existing quantum computers. The decomposition can be used as an aggressive optimization method for the whole circuit, as well as to test part of an algorithm on a quantum accelerator. For the selection and implementation of the decomposition algorithm, perfect qubits are assumed. We base our decomposition technique on Quantum Shannon Decomposition, which generates O(344n) controlled-not gates for an n-qubit input gate. In addition, we implement optimizations to take advantage of the potential underlying structure in the input or intermediate matrices, as well as to minimize the execution time of the decomposition. Comparing our implementation to Qubiter and the UniversalQCompiler (UQC), we show that our implementation generates circuits that are much shorter than those of Qubiter and not much longer than the UQC. At the same time, it is also up to 10 times as fast as Qubiter and about 500 times as fast as the UQC. Full article
(This article belongs to the Special Issue Quantum Software Engineering and Programming)
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21 pages, 3159 KiB  
Article
Variational Quantum Circuits for Machine Learning. An Application for the Detection of Weak Signals
by Israel Griol-Barres, Sergio Milla, Antonio Cebrián, Yashar Mansoori and José Millet
Appl. Sci. 2021, 11(14), 6427; https://doi.org/10.3390/app11146427 - 12 Jul 2021
Cited by 10 | Viewed by 4338
Abstract
Quantum computing is a new paradigm for a multitude of computing applications. This study presents the technologies that are currently available for the physical implementation of qubits and quantum gates, establishing their main advantages and disadvantages and the available frameworks for programming and [...] Read more.
Quantum computing is a new paradigm for a multitude of computing applications. This study presents the technologies that are currently available for the physical implementation of qubits and quantum gates, establishing their main advantages and disadvantages and the available frameworks for programming and implementing quantum circuits. One of the main applications for quantum computing is the development of new algorithms for machine learning. In this study, an implementation of a quantum circuit based on support vector machines (SVMs) is described for the resolution of classification problems. This circuit is specially designed for the noisy intermediate-scale quantum (NISQ) computers that are currently available. As an experiment, the circuit is tested on a real quantum computer based on superconducting qubits for an application to detect weak signals of the future. Weak signals are indicators of incipient changes that will have a future impact. Even for experts, the detection of these events is complicated since it is too early to predict this impact. The data obtained with the experiment shows promising results but also confirms that ongoing technological development is still required to take full advantage of quantum computing. Full article
(This article belongs to the Special Issue Quantum Software Engineering and Programming)
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18 pages, 2417 KiB  
Article
On the Definition of Quantum Programming Modules
by Pedro Sánchez and Diego Alonso
Appl. Sci. 2021, 11(13), 5843; https://doi.org/10.3390/app11135843 - 23 Jun 2021
Cited by 7 | Viewed by 2105
Abstract
There are no doubts that quantum programming and, in general, quantum computing, is one of the most promising areas within computer science and one of the areas where most expectations are being placed in recent years. Although the days when reliable and affordable [...] Read more.
There are no doubts that quantum programming and, in general, quantum computing, is one of the most promising areas within computer science and one of the areas where most expectations are being placed in recent years. Although the days when reliable and affordable quantum computers will be available is still a long way off, the explosion of programming languages for quantum programming has grown exponentially in recent years. The software engineering community has been quick to react to the need to adopt and adapt well-known tools and methods for software development, and for the design of new ones tailored to this new programming paradigm. However, many key aspects for its success depend on the establishment of an appropriate conceptual framework for the conception and design of quantum programs. This article discusses the concept of module, key in the software engineering discipline, and establishes initial criteria for determining the cohesion and coupling levels of a module in the field of quantum programming as a first step towards a sound quantum software engineering. As detailed in the article, the conceptual differences between classical and quantum computing are so pronounced that the translation of classical concepts to the new programming approach is not straightforward. Full article
(This article belongs to the Special Issue Quantum Software Engineering and Programming)
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