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Electronics
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Quantum Information, Computation and Cryptography
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Quantum Information, Computation and Cryptography

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Quantum Electronics".

Deadline for manuscript submissions: closed (15 November 2023) | Viewed by 12462

Special Issue Editors

Dr. He-Liang Huang

E-Mail Website
Guest Editor
1. Henan Key Laboratory of Quantum Information and Cryptography, University of Science and Technology of China, Hefei 230026, China
2. Shanghai Research Center for Quantum Sciences, Shanghai 201315, China
Interests: quantum machine learning; secure cloud quantum computing; big data quantum computing; linear optical quantum computing; superconducting quantum computing
Dr. Chu Guo

E-Mail Website
Guest Editor
Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education, Department of Physics and Synergetic Innovation Center for Quantum Effects and Applications, Hunan Normal University, Changsha 410081, China
Interests: tensor network states; quantum computing; quantum machine learning
Dr. Zu-En Su

E-Mail Website
Guest Editor
Department of Physics, Technion - Israel Institute of Technology, Haifa 3200003, Israel
Interests: quantum information; solid state physics; multiphoton interference and entanglement
Dr. Shi-Lei Su

E-Mail Website
Guest Editor
School of Physics and Microelectronics, Zhengzhou University, Zhengzhou 450001, China
Interests: geometric quantum computation; rydberg atoms; optimal control

Special Issue Information

Dear Colleagues,

The second quantum revolution was founded on fundamental research at the intersection of physics and information science, giving rise to the field we today refer to as quantum information science.  In the past few decades, tremendous advances have been made in practical quantum information technologies, including quantum cryptography, quantum computing, and quantum metrology. It seems that we will soon enter the era of scientific and technological applications of quantum technology.

This Special Issue will focus on quantum information science, especially quantum communication and quantum computing. It will aim to provide the up-to-date findings in theories and experiments of quantum information science for a broad range of readers.

Topics of interest of this Special Issue include, but are not limited to, the following:

  • Quantum algorithms;
  • Quantum computing;
  • Quantum error correcting codes;
  • Quantum communication;
  • Quantum Shannon theory;
  • Quantum complexity theory;
  • Quantum networks;
  • Quantum cryptography;
  • Quantum information processing;
  • Quantum metrology and sensing;
  • Quantum programing;
  • Quantum machine learning;
  • Blind quantum computing;
  • Tensor network states;
  • Quantum simulations;
  • Quantum foundations;
  • Quantum optics
  • Geometric quantum computation

Dr. He-Liang Huang
Dr. Chu Guo
Dr. Zu-En Su
Dr. Shi-Lei Su
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. Electronics 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.

Keywords

  • quantum information
  • quantum computing
  • quantum algorithms
  • quantum communication
  • quantum metrology
  • quantum programing
  • quantum error correcting codes
  • quantum machine learning
  • quantum complexity theory
  • tensor network states

Published Papers (5 papers)

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Research

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7 pages, 686 KiB  
Communication
Optimized Polarization Encoder with High Extinction Ratio for Quantum Key Distribution System
by Pengcheng Wang, Qianqian Zhang, Huanwen Xie and Banghong Guo
Electronics 2023, 12(8), 1859; https://doi.org/10.3390/electronics12081859 - 14 Apr 2023
Viewed by 1384
Abstract
Polarization encoding is a promising approach for practical quantum key distribution (QKD) systems due to its simple encoding and decoding methodology. In this study, we propose a self-compensating polarization encoder (SCPE) based on a phase modulator, which can be composed of commercial off-the-shelf [...] Read more.
Polarization encoding is a promising approach for practical quantum key distribution (QKD) systems due to its simple encoding and decoding methodology. In this study, we propose a self-compensating polarization encoder (SCPE) based on a phase modulator, which can be composed of commercial off-the-shelf (COT) devices. We conducted a proof-of-concept experiment to test the SCPE, which demonstrated an in-system quantum bit error rate (QBER) of 0.53% and long-term running stability without any active adjustments. Additionally, we conducted experiments with transmission over commercial fiber spools of lengths up to 100 km and obtained a secure finite key rate of 3 kbps. Our polarization encoder is a promising solution for various polarization encoding protocols, including BB84, MDI, and RFI. Full article
(This article belongs to the Special Issue Quantum Information, Computation and Cryptography)
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10 pages, 555 KiB  
Communication
Reconstructing Quantum States from Sparse Measurements
by Yufan Xie, Chu Guo and Zhihui Peng
Electronics 2023, 12(5), 1096; https://doi.org/10.3390/electronics12051096 - 22 Feb 2023
Viewed by 1115
Abstract
Quantum state tomography (QST) is a central technique to fully characterize an unknown quantum state. However, standard QST requires an exponentially growing number of quantum measurements against the system size, which limits its application to smaller systems. Here, we explore the sparsity of [...] Read more.
Quantum state tomography (QST) is a central technique to fully characterize an unknown quantum state. However, standard QST requires an exponentially growing number of quantum measurements against the system size, which limits its application to smaller systems. Here, we explore the sparsity of underlying quantum state and propose a QST scheme that combines the matrix product states’ representation of the quantum state with a supervised machine learning algorithm. Our method could reconstruct the unknown sparse quantum states with very high precision using only a portion of the measurement data in a randomly selected basis set. In particular, we demonstrate that the Wolfgang states could be faithfully reconstructed using around 25% of the whole basis, and that the randomly generated quantum states, which could be efficiently represented as matrix product states, could be faithfully reconstructed using a number of bases that scales sub-exponentially against the system size. Full article
(This article belongs to the Special Issue Quantum Information, Computation and Cryptography)
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8 pages, 827 KiB  
Article
Warm Starting Variational Quantum Algorithms with Near Clifford Circuits
by Yun-Fei Niu, Shuo Zhang and Wan-Su Bao
Electronics 2023, 12(2), 347; https://doi.org/10.3390/electronics12020347 - 09 Jan 2023
Cited by 1 | Viewed by 1458
Abstract
As a mainstream approach in the quantum machine learning field, variational quantum algorithms (VQAs) are frequently mentioned among the most promising applications for quantum computing. However, VQAs suffer from inefficient training methods. Here, we propose a pretraining strategy named near Clifford circuits warm [...] Read more.
As a mainstream approach in the quantum machine learning field, variational quantum algorithms (VQAs) are frequently mentioned among the most promising applications for quantum computing. However, VQAs suffer from inefficient training methods. Here, we propose a pretraining strategy named near Clifford circuits warm start (NCC-WS) to find the initialization for parameterized quantum circuits (PQCs) in VQAs. We explored the expressibility of NCCs and the correlation between the expressibility and acceleration. The achieved results suggest that NCC-WS can find the correct initialization for the training of VQAs to achieve acceleration. Full article
(This article belongs to the Special Issue Quantum Information, Computation and Cryptography)
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5 pages, 526 KiB  
Article
Double C-NOT Attack on a Single-State Semi-Quantum Key Distribution Protocol and Its Improvement
by Jun Gu and Tzonelih Hwang
Electronics 2022, 11(16), 2522; https://doi.org/10.3390/electronics11162522 - 12 Aug 2022
Cited by 2 | Viewed by 1010
Abstract
Recently, Zhang et al. proposed a single-state semi-quantum key distribution protocol to help a quantum participant share a secret key with a classical participant. However, this study shows that an eavesdropper can use a double C-NOT attack to obtain parts of the final [...] Read more.
Recently, Zhang et al. proposed a single-state semi-quantum key distribution protocol to help a quantum participant share a secret key with a classical participant. However, this study shows that an eavesdropper can use a double C-NOT attack to obtain parts of the final shared key without being detected by the participants. To avoid this problem, a modification is proposed here. Full article
(This article belongs to the Special Issue Quantum Information, Computation and Cryptography)
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Review

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21 pages, 5890 KiB  
Review
Quantum Machine Learning—An Overview
by Kyriaki A. Tychola, Theofanis Kalampokas and George A. Papakostas
Electronics 2023, 12(11), 2379; https://doi.org/10.3390/electronics12112379 - 24 May 2023
Cited by 7 | Viewed by 6281
Abstract
Quantum computing has been proven to excel in factorization issues and unordered search problems due to its capability of quantum parallelism. This unique feature allows exponential speed-up in solving certain problems. However, this advantage does not apply universally, and challenges arise when combining [...] Read more.
Quantum computing has been proven to excel in factorization issues and unordered search problems due to its capability of quantum parallelism. This unique feature allows exponential speed-up in solving certain problems. However, this advantage does not apply universally, and challenges arise when combining classical and quantum computing to achieve acceleration in computation speed. This paper aims to address these challenges by exploring the current state of quantum machine learning and benchmarking the performance of quantum and classical algorithms in terms of accuracy. Specifically, we conducted experiments with three datasets for binary classification, implementing Support Vector Machine (SVM) and Quantum SVM (QSVM) algorithms. Our findings suggest that the QSVM algorithm outperforms classical SVM on complex datasets, and the performance gap between quantum and classical models increases with dataset complexity, as simple models tend to overfit with complex datasets. While there is still a long way to go in terms of developing quantum hardware with sufficient resources, quantum machine learning holds great potential in areas such as unsupervised learning and generative models. Moving forward, more efforts are needed to explore new quantum learning models that can leverage the power of quantum mechanics to overcome the limitations of classical machine learning. Full article
(This article belongs to the Special Issue Quantum Information, Computation and Cryptography)
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