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Algorithms
Special Issues
Algorithms for Quantum Computing and Quantum-Centric High-Performance Computing
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Algorithms for Quantum Computing and Quantum-Centric High-Performance Computing

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms for Multidisciplinary Applications".

Deadline for manuscript submissions: 31 August 2025 | Viewed by 2954

Special Issue Editors

Dr. Esam El-Araby

E-Mail Website
Guest Editor
Department of Electrical Engineering and Computer Science (EECS), University of Kansas (KU), Lawrence, KS 66045, USA
Interests: computer architecture; reconfigurable computing; quantum computing; quantum communications; reversible computing; heterogeneous computing; biologically-inspired and neuromorphic architectures; evolvable hardware
Dr. Naveed Mahmud

E-Mail Website
Co-Guest Editor
College of Engineering and Science—Electrical Engineering and Computer Science, Florida Institute of Technology, Melbourne, FL 32901, USA
Interests: reconfigurable computing; quantum computing; hybrid quantum-classical architectures

Special Issue Information

Dear Colleagues,

Quantum computing is an emerging technology that promises to achieve major scientific breakthroughs. Quantum-Centric High-Performance Computing (QC-HPC) is the next wave of computing that will combine the power of Quantum Computing (QC) and High-Performance Computing (HPC). Integrating quantum processors in HPC environments will lead the way towards useful quantum computing and solution of real-world challenging problems. Development of QC-HPCs will require extensive research at the system architecture and algorithm level. This special issue of Algorithms seeks to compile state-of-the-art research that showcases the latest algorithmic developments, both theoretical and experimental, in the emerging field of Quantum Computing and Quantum-Centric High-Performance Computing. Submissions are welcome on a range of quantum algorithms and quantum-classical algorithms deployable on quantum devices or hybrid QC-HPC systems, respectively. Topics include quantum algorithms for machine learning and artificial intelligence, hybrid algorithms for QC-HPC systems, hybrid programming models, tools and environments, QC systems in data center facilities, and software for HPC/QC. In addition, emulation or simulation of quantum circuits/algorithms deployed in HPC systems are also encouraged. This Special Issue aims to emphasize and identify significant strides made in QC and QC-HPC, bridging the gap between theory and practical applications and generating attention from researchers and practitioners alike.

Dr. Esam El-Araby
Dr. Naveed Mahmud
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. Algorithms is an international peer-reviewed open access monthly 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 1600 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 natural language processing
  • quantum-centric high-performance computing
  • quantum software, simulation, and emulation
  • hybrid algorithms for QC-HPC
  • hybrid programming models, tools, software for QC-HPC
  • QC in data centers

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Published Papers (3 papers)

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Research

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32 pages, 5246 KiB  
Article
Quantum Circuit Synthesis Using Fuzzy-Logic-Assisted Genetic Algorithms
by Ishraq Islam, Vinayak Jha, Sneha Thomas, Kieran F. Egan, Alvir Nobel, Serom Kim, Manu Chaudhary, Sunday Ogundele, Dylan Kneidel, Ben Phillips, Manish Singh, Kareem El-Araby, Devon Bontrager and Esam El-Araby
Algorithms 2025, 18(4), 178; https://doi.org/10.3390/a18040178 - 21 Mar 2025
Viewed by 279
Abstract
Quantum algorithms will likely play a key role in future high-performance-computing (HPC) environments. These algorithms are typically expressed as quantum circuits composed of arbitrary gates or as unitary matrices. Executing these on physical devices, however, requires translation to device-compatible circuits, in a process [...] Read more.
Quantum algorithms will likely play a key role in future high-performance-computing (HPC) environments. These algorithms are typically expressed as quantum circuits composed of arbitrary gates or as unitary matrices. Executing these on physical devices, however, requires translation to device-compatible circuits, in a process called quantum compilation or circuit synthesis, since these devices support a limited number of native gates. Moreover, these devices typically have specific qubit topologies, which constrain how and where gates can be applied. Consequently, logical qubits in input circuits and unitaries may need to be mapped to and routed between physical qubits. Furthermore, current Noisy Intermediate-Scale Quantum (NISQ) devices present additional constraints. They are vulnerable to errors during gate application and their short decoherence times lead to qubits rapidly succumbing to accumulated noise and possibly corrupting computations. Therefore, circuits synthesized for NISQ devices need to minimize gates and execution times. The problem of synthesizing device-compatible circuits, while optimizing for low gate count and short execution times, can be shown to be computationally intractable using analytical methods. Therefore, interest has grown towards heuristics-based synthesis techniques, which are able to produce approximations of the desired algorithm, while optimizing depth and gate-count. In this work, we investigate using genetic algorithms (GA)—a proven gradient-free optimization technique based on natural selection—for circuit synthesis. In particular, we formulate the quantum synthesis problem as a multi-objective optimization (MOO) problem, with the objectives of minimizing the approximation error, number of multi-qubit gates, and circuit depth. We also employ fuzzy logic for runtime parameter adaptation of GA to enhance search efficiency and solution quality in our proposed method. Full article
(This article belongs to the Special Issue Algorithms for Quantum Computing and Quantum-Centric High-Performance Computing)
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17 pages, 4690 KiB  
Article
Advantages of Density in Tensor Network Geometries for Gradient-Based Training
by Sergi Masot-Llima and Artur Garcia-Saez
Algorithms 2025, 18(2), 70; https://doi.org/10.3390/a18020070 - 31 Jan 2025
Viewed by 848
Abstract
Tensor networks are a very powerful data structure tool originating from simulations of quantum systems. In recent years, they have seen increased use in machine learning, mostly in trainings with gradient-based techniques, due to their flexibility and performance achieved by exploiting hardware acceleration. [...] Read more.
Tensor networks are a very powerful data structure tool originating from simulations of quantum systems. In recent years, they have seen increased use in machine learning, mostly in trainings with gradient-based techniques, due to their flexibility and performance achieved by exploiting hardware acceleration. As ansatzes, tensor networks can be used with flexible geometries, and it is known that for highly regular ones, their dimensionality has a large impact on performance and representation power. For heterogeneous structures, however, these effects are not completely characterized. In this article, we train tensor networks with different geometries to encode a random quantum state, and see that densely connected structures achieve better infidelities than more sparse structures, with higher success rates and less time. Additionally, we give some general insight on how to improve the memory requirements of these sparse structures and the impact of such improvement on the trainings. Finally, as we use HPC resources for the calculations, we discuss the requirements for this approach and showcase performance improvements with GPU acceleration on a last-generation supercomputer. Full article
(This article belongs to the Special Issue Algorithms for Quantum Computing and Quantum-Centric High-Performance Computing)
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Review

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19 pages, 2026 KiB  
Review
Quantum Computing and Machine Learning in Medical Decision-Making: A Comprehensive Review
by James C. L. Chow
Algorithms 2025, 18(3), 156; https://doi.org/10.3390/a18030156 - 9 Mar 2025
Cited by 2 | Viewed by 1351
Abstract
Medical decision-making is increasingly integrating quantum computing (QC) and machine learning (ML) to analyze complex datasets, improve diagnostics, and enable personalized treatments. While QC holds the potential to accelerate optimization, drug discovery, and genomic analysis as hardware capabilities advance, current implementations remain limited [...] Read more.
Medical decision-making is increasingly integrating quantum computing (QC) and machine learning (ML) to analyze complex datasets, improve diagnostics, and enable personalized treatments. While QC holds the potential to accelerate optimization, drug discovery, and genomic analysis as hardware capabilities advance, current implementations remain limited compared to classical computing in many practical applications. Meanwhile, ML has already demonstrated significant success in medical imaging, predictive modeling, and decision support. Their convergence, particularly through quantum machine learning (QML), presents opportunities for future advancements in processing high-dimensional healthcare data and improving clinical outcomes. This review examines the foundational concepts, key applications, and challenges of these technologies in healthcare, explores their potential synergy in solving clinical problems, and outlines future directions for quantum-enhanced ML in medical decision-making. Full article
(This article belongs to the Special Issue Algorithms for Quantum Computing and Quantum-Centric High-Performance Computing)
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