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Mathematics
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Quantum Computing and Networking
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Quantum Computing and Networking

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

Deadline for manuscript submissions: 18 April 2025 | Viewed by 3057

Special Issue Editors

Prof. Dr. Jehn-Ruey Jiang

E-Mail Website
Guest Editor
Department of Computer Science and Information Engineering, National Central University, Taoyuan City 320317, Taiwan
Interests: quantum computing; quantum networking; quantum algorithms; machine learning/deep learning for smart manufacturing; Internet of Things
Special Issues, Collections and Topics in MDPI journals
Dr. YungYu Zhang

E-Mail Website
Guest Editor
Department of Computer Science and Information Engineering, National Central University, Taoyuan City 320317, Taiwan
Interests: programming languages; programming education; software engineering; high-performance computing and scientific computing; programming in machine learning; data science; quantum computing

Special Issue Information

Dear Colleagues,

Quantum computers such as IBM Q, Google Sycamore, and D-WAVE Advantage are built based on the phenomena of quantum superposition, quantum entanglement, and quantum tunneling. They perform computation on the basis of quantum bits or qubits. In contrast, traditional or classical computers perform computation on the basis of bits. A bit is either 0 or 1, but a qubit exists in a superposition of both 0 and 1, and only when measured does it clearly reveal the 0 or 1 state. Since the computing power of a quantum computer increases exponentially with the number of qubits, it has a computing power that cannot be surpassed by a classical computer, which is called quantum supremacy.

Based on technologies such as quantum-entanglement generation, quantum-entanglement swap, and quantum teleportation, quantum computers and classical computers are even interconnected to form quantum networks offering functionalities that classical networks cannot offer. For example, with quantum networking and quantum key distribution mechanisms, information can be transmitted or teleported between computers to achieve unhackable unconditional security. Furthermore, quantum computers can be clustered together to perform distributed quantum computation by accumulating their qubits together.

This Special Issue solicits submissions of papers related to quantum computing and quantum networking. The topics include but are not limited to quantum algorithms, quantum annealing algorithms, quantum-inspired algorithms, distributed quantum algorithms, quantum machine learning, quantum neural networks, quantum key distribution, quantum network routing, quantum network communication, and quantum network protocol designs, and quantum Internet of Things (QIoT).

Prof. Dr. Jehn-Ruey Jiang
Dr. YungYu Zhang
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.

Keywords

  • quantum algorithms
  • quantum annealing algorithms
  • quantum-inspired algorithms
  • distributed quantum algorithms
  • quantum machine learning
  • quantum neural networks
  • quantum key distribution
  • quantum network routing
  • quantum network communication
  • quantum network protocol designs
  • quantum Internet of Things (QIoT)

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Published Papers (2 papers)

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21 pages, 7152 KiB  
Article
Error Mitigation in the NISQ Era: Applying Measurement Error Mitigation Techniques to Enhance Quantum Circuit Performance
by Misha Urooj Khan, Muhammad Ahmad Kamran, Wajiha Rahim Khan, Malik Muhammad Ibrahim, Muhammad Umair Ali and Seung Won Lee
Mathematics 2024, 12(14), 2235; https://doi.org/10.3390/math12142235 - 17 Jul 2024
Viewed by 1236
Abstract
In quantum computing, noisy intermediate-scale quantum (NISQ) devices offer unprecedented computational capabilities but are vulnerable to errors, notably measurement inaccuracies that impact computation accuracy. This study explores the efficacy of error mitigation techniques in improving quantum circuit performance on NISQ devices. Techniques such [...] Read more.
In quantum computing, noisy intermediate-scale quantum (NISQ) devices offer unprecedented computational capabilities but are vulnerable to errors, notably measurement inaccuracies that impact computation accuracy. This study explores the efficacy of error mitigation techniques in improving quantum circuit performance on NISQ devices. Techniques such as dynamic decoupling (DD), twirled readout error extraction (T-REx) and zero-noise extrapolation (ZNE) are examined through extensive experimentation on an ideal simulator, IBM Kyoto, and IBM Osaka quantum computers. Results reveal significant performance discrepancies across scenarios, with error mitigation techniques notably enhancing both estimator result and variance values, aligning more closely with ideal simulator outcomes. The comparison results with ideal simulator (having expected result value 0.8284) shows that T-Rex has improved results on IBM Kyoto and enhanced average expected result value from 0.09 to 0.35. Similarly, DD has improved average expected result values from 0.2492 to 0.3788 on IBM Osaka. These findings underscore the critical role of error mitigation in bolstering quantum computation reliability. The results suggest that selection of mitigation technique depends upon quantum circuit and its depth, type of hardware and operations to be performed. Full article
(This article belongs to the Special Issue Quantum Computing and Networking)
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25 pages, 728 KiB  
Article
Quantum K-Nearest Neighbors: Utilizing QRAM and SWAP-Test Techniques for Enhanced Performance
by Alberto Maldonado-Romo, J. Yaljá Montiel-Pérez, Victor Onofre, Javier Maldonado-Romo  and Juan Humberto Sossa-Azuela 
Mathematics 2024, 12(12), 1872; https://doi.org/10.3390/math12121872 - 16 Jun 2024
Viewed by 1013
Abstract
This work introduces a quantum K-Nearest Neighbor (K-NN) classifier algorithm. The algorithm utilizes angle encoding through a Quantum Random Access Memory (QRAM) using n number of qubit addresses with O(log(n)) space complexity. It incorporates Grover’s algorithm and [...] Read more.
This work introduces a quantum K-Nearest Neighbor (K-NN) classifier algorithm. The algorithm utilizes angle encoding through a Quantum Random Access Memory (QRAM) using n number of qubit addresses with O(log(n)) space complexity. It incorporates Grover’s algorithm and the quantum SWAP-Test to identify similar states and determine the nearest neighbors with high probability, achieving Om search complexity, where m is the qubit address. We implement a simulation of the algorithm using IBM’s Qiskit with GPU support, applying it to the Iris and MNIST datasets with two different angle encodings. The experiments employ multiple QRAM cell sizes (8, 16, 32, 64, 128) and perform ten trials per size. According to the performance, accuracy values in the Iris dataset range from 89.3 ± 5.78% to 94.0 ± 1.56%. The MNIST dataset’s mean binary accuracy values range from 79.45 ± 18.84% to 94.00 ± 2.11% for classes 0 and 1. Additionally, a comparison of the results of this proposed approach with different state-of-the-art versions of QK-NN and the classical K-NN using Scikit-learn. This method achieves a 96.4 ± 2.22% accuracy in the Iris dataset. Finally, this proposal contributes an experimental result to the state of the art for the MNIST dataset, achieving an accuracy of 96.55 ± 2.00%. This work presents a new implementation proposal for QK-NN and conducts multiple experiments that yield more robust results than previous implementations. Although our average performance approaches still need to surpass the classic results, an experimental increase in the size of QRAM or the amount of data to encode is not achieved due to limitations. However, our results show promising improvement when considering working with more feature numbers and accommodating more data in the QRAM. Full article
(This article belongs to the Special Issue Quantum Computing and Networking)
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