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Information
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Quantum Information Processing and Machine Learning
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Quantum Information Processing and Machine Learning

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Theory and Methodology".

Deadline for manuscript submissions: 30 April 2025 | Viewed by 29443

Special Issue Editors

Dr. Wenbin Yu

E-Mail Website
Guest Editor
School of Software, Nanjing University of Information Science & Technology, Nanjing 210044, China
Interests: quantum information processing; machine learning
Dr. Yadang Chen

E-Mail Website
Guest Editor
School of Computer Science, Nanjing University of Information Science & Technology, Nanjing 210044, China
Interests: computer vision; deep learning
Dr. Chengjun Zhang

E-Mail Website
Guest Editor
School of Computer Science, Nanjing University of Information Science & Technology, Nanjing 210044, China
Interests: complex networks; information filtering; deep learning

Special Issue Information

Dear Colleagues,

Quantum information processing is a field that combines the principles of quantum mechanics and information science to study the processing, analysis, and transmission of information. It covers both theoretical and experimental aspects of quantum physics, including the limits that quantum information can reach. Moreover, driven by ever-increasing computer power and algorithmic advances, machine learning techniques have become a powerful tool for finding patterns in data. Machine learning has become a ubiquitous and effective technique for data processing and classification. Due to the advantages and advances in quantum computing in many fields (e.g., cryptography, machine learning, healthcare), the combination of classical machine learning and quantum information processing has established a new field called, quantum machine learning, which has become an important research topic in academia.

This Special Issue aims to curate original research and review articles from academia and industry-relevant researchers in the fields of quantum machine learning, quantum information processing, machine learning, and deep learning. image processing, computer vision, natural language processing, and recommendation system. Researchers and industry practitioners from academia are invited to submit their innovative research on technical challenges and state-of-the-art findings related to quantum information processing and machine learning. This Special Issue encourages authors to discuss and express their views on current trends, challenges, and state-of-the-art solutions to various problems in quantum machine learning.

Topics of interest include but are not limited to:

  1. Quantum machine learning;
  2. Quantum computing;
  3. Quantum cryptography and communications;
  4. Quantum algorithms;
  5. Machine learning;
  6. Deep learning;
  7. Image processing;
  8. Computer vision;
  9. Natural language processing;
  10. Recommendations system.

Dr. Wenbin Yu
Dr. Yadang Chen
Dr. Chengjun 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. Information 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 machine learning
  • quantum information
  • quantum computation
  • quantum communication
  • machine learning
  • deep learning
  • computer vision

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Published Papers (10 papers)

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Research

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13 pages, 3490 KiB  
Article
QSA-QConvLSTM: A Quantum Computing-Based Approach for Spatiotemporal Sequence Prediction
by Wenbin Yu, Zongyuan Chen, Chengjun Zhang and Yadang Chen
Information 2025, 16(3), 206; https://doi.org/10.3390/info16030206 - 6 Mar 2025
Viewed by 544
Abstract
The ability to capture long-distance dependencies is critical for improving the prediction accuracy of spatiotemporal prediction models. Traditional ConvLSTM models face inherent limitations in this regard, along with the challenge of information decay, which negatively impacts prediction performance. To address these issues, this [...] Read more.
The ability to capture long-distance dependencies is critical for improving the prediction accuracy of spatiotemporal prediction models. Traditional ConvLSTM models face inherent limitations in this regard, along with the challenge of information decay, which negatively impacts prediction performance. To address these issues, this paper proposes a QSA-QConvLSTM model, which integrates quantum convolution circuits and quantum self-attention mechanisms. The quantum self-attention mechanism maps query, key, and value vectors using variational quantum circuits, effectively enhancing the ability to model long-distance dependencies in spatiotemporal data. Additionally, the use of quantum convolution circuits improves the extraction of spatial features. Experiments on the Moving MNIST dataset demonstrate the superiority of the QSA-QConvLSTM model over existing models, including ConvLSTM, TrajGRU, PredRNN, and PredRNN v2, with MSE and SSIM scores of 44.3 and 0.906, respectively. Ablation studies further verify the effectiveness and necessity of the quantum convolution circuits and quantum self-attention modules, providing an efficient and accurate approach to quantized modeling for spatiotemporal prediction tasks. Full article
(This article belongs to the Special Issue Quantum Information Processing and Machine Learning)
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18 pages, 399 KiB  
Article
Quantum Algorithms for the Multiplication of Circulant Matrices and Vectors
by Lu Hou, Zhenyu Huang and Chang Lv
Information 2024, 15(8), 453; https://doi.org/10.3390/info15080453 - 1 Aug 2024
Viewed by 1576
Abstract
This article presents two quantum algorithms for computing the product of a circulant matrix and a vector. The arithmetic complexity of the first algorithm is O(Nlog2N) in most cases. For the second algorithm, when the entries in [...] Read more.
This article presents two quantum algorithms for computing the product of a circulant matrix and a vector. The arithmetic complexity of the first algorithm is O(Nlog2N) in most cases. For the second algorithm, when the entries in the circulant matrix and the vector take values in C or R, the complexity is O(Nlog2N) in most cases. However, when these entries take values from positive real numbers, the complexity is reduced to O(log3N) in most cases, which presents an exponential speedup compared to the classical complexity of O(NlogN) for computing the product of a circulant matrix and vector. We apply this algorithm to the convolution calculation in quantum convolutional neural networks, which effectively accelerates the computation of convolutions. Additionally, we present a concrete quantum circuit structure for quantum convolutional neural networks. Full article
(This article belongs to the Special Issue Quantum Information Processing and Machine Learning)
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29 pages, 1097 KiB  
Article
Control of Qubit Dynamics Using Reinforcement Learning
by Dimitris Koutromanos, Dionisis Stefanatos and Emmanuel Paspalakis
Information 2024, 15(5), 272; https://doi.org/10.3390/info15050272 - 11 May 2024
Cited by 5 | Viewed by 1882
Abstract
The progress in machine learning during the last decade has had a considerable impact on many areas of science and technology, including quantum technology. This work explores the application of reinforcement learning (RL) methods to the quantum control problem of state transfer in [...] Read more.
The progress in machine learning during the last decade has had a considerable impact on many areas of science and technology, including quantum technology. This work explores the application of reinforcement learning (RL) methods to the quantum control problem of state transfer in a single qubit. The goal is to create an RL agent that learns an optimal policy and thus discovers optimal pulses to control the qubit. The most crucial step is to mathematically formulate the problem of interest as a Markov decision process (MDP). This enables the use of RL algorithms to solve the quantum control problem. Deep learning and the use of deep neural networks provide the freedom to employ continuous action and state spaces, offering the expressivity and generalization of the process. This flexibility helps to formulate the quantum state transfer problem as an MDP in several different ways. All the developed methodologies are applied to the fundamental problem of population inversion in a qubit. In most cases, the derived optimal pulses achieve fidelity equal to or higher than 0.9999, as required by quantum computing applications. The present methods can be easily extended to quantum systems with more energy levels and may be used for the efficient control of collections of qubits and to counteract the effect of noise, which are important topics for quantum sensing applications. Full article
(This article belongs to the Special Issue Quantum Information Processing and Machine Learning)
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22 pages, 479 KiB  
Article
A Quantum Approach to News Verification from the Perspective of a News Aggregator
by Theodore Andronikos and Alla Sirokofskich
Information 2024, 15(4), 207; https://doi.org/10.3390/info15040207 - 6 Apr 2024
Cited by 1 | Viewed by 1801
Abstract
In the dynamic landscape of digital information, the rise of misinformation and fake news presents a pressing challenge. This paper takes a completely new approach to verifying news, inspired by how quantum actors can reach agreement even when they are spatially spread out. [...] Read more.
In the dynamic landscape of digital information, the rise of misinformation and fake news presents a pressing challenge. This paper takes a completely new approach to verifying news, inspired by how quantum actors can reach agreement even when they are spatially spread out. We propose a radically new—to the best of our knowledge—algorithm that uses quantum “entanglement” (think of it as a special connection) to help news aggregators “sniff out” bad actors, whether they are other news sources or even fact-checkers trying to spread misinformation. This algorithm does not rely on quantum signatures; it merely uses basic quantum technology which we already have, in particular, special pairs of particles called “EPR pairs” that are much easier to create than other options. More elaborate entangled states are like juggling too many balls—they are difficult to make and slow things down, especially when many players are involved. So, we adhere to Bell states, the simplest form of entanglement, which are easy to generate no matter how many players are involved. This means that our algorithm is faster to set up, works for any number of participants, and is more practical for real-world use. Additionally, as a “bonus point”, it finishes in a fixed number of steps, regardless of how many players are involved, making it even more scalable. This new approach may lead to a powerful and efficient way to fight misinformation in the digital age, using the weird and wonderful world of quantum mechanics. Full article
(This article belongs to the Special Issue Quantum Information Processing and Machine Learning)
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17 pages, 391 KiB  
Article
QISS: Quantum-Enhanced Sustainable Security Incident Handling in the IoT
by Carlos Blanco, Antonio Santos-Olmo and Luis Enrique Sánchez
Information 2024, 15(4), 181; https://doi.org/10.3390/info15040181 - 27 Mar 2024
Cited by 3 | Viewed by 1993
Abstract
As the Internet of Things (IoT) becomes more integral across diverse sectors, including healthcare, energy provision and industrial automation, the exposure to cyber vulnerabilities and potential attacks increases accordingly. Facing these challenges, the essential function of an Information Security Management System (ISMS) in [...] Read more.
As the Internet of Things (IoT) becomes more integral across diverse sectors, including healthcare, energy provision and industrial automation, the exposure to cyber vulnerabilities and potential attacks increases accordingly. Facing these challenges, the essential function of an Information Security Management System (ISMS) in safeguarding vital information assets comes to the fore. Within this framework, risk management is key, tasked with the responsibility of adequately restoring the system in the event of a cybersecurity incident and evaluating potential response options. To achieve this, the ISMS must evaluate what is the best response. The time to implement a course of action must be considered, as the period required to restore the ISMS is a crucial factor. However, in an environmentally conscious world, the sustainability dimension should also be considered to choose more sustainable responses. This paper marks a notable advancement in the fields of risk management and incident response, integrating security measures with the wider goals of sustainability and corporate responsibility. It introduces a strategy for handling cybersecurity incidents that considers both the response time and sustainability. This approach provides the flexibility to prioritize either the response time, sustainability or a balanced mix of both, according to specific preferences, and subsequently identifies the most suitable actions to re-secure the system. Employing a quantum methodology, it guarantees reliable and consistent response times, independent of the incident volume. The practical application of this novel method through our framework, MARISMA, is demonstrated in real-world scenarios, underscoring its efficacy and significance in the contemporary landscape of risk management. Full article
(This article belongs to the Special Issue Quantum Information Processing and Machine Learning)
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13 pages, 2017 KiB  
Article
Quantum Convolutional Long Short-Term Memory Based on Variational Quantum Algorithms in the Era of NISQ
by Zeyu Xu, Wenbin Yu, Chengjun Zhang and Yadang Chen
Information 2024, 15(4), 175; https://doi.org/10.3390/info15040175 - 22 Mar 2024
Cited by 1 | Viewed by 2490
Abstract
In the era of noisy intermediate-scale quantum (NISQ) computing, the synergistic collaboration between quantum and classical computing models has emerged as a promising solution for tackling complex computational challenges. Long short-term memory (LSTM), as a popular network for modeling sequential data, has been [...] Read more.
In the era of noisy intermediate-scale quantum (NISQ) computing, the synergistic collaboration between quantum and classical computing models has emerged as a promising solution for tackling complex computational challenges. Long short-term memory (LSTM), as a popular network for modeling sequential data, has been widely acknowledged for its effectiveness. However, with the increasing demand for data and spatial feature extraction, the training cost of LSTM exhibits exponential growth. In this study, we propose the quantum convolutional long short-term memory (QConvLSTM) model. By ingeniously integrating classical convolutional LSTM (ConvLSTM) networks and quantum variational algorithms, we leverage the variational quantum properties and the accelerating characteristics of quantum states to optimize the model training process. Experimental validation demonstrates that, compared to various LSTM variants, our proposed QConvLSTM model outperforms in terms of performance. Additionally, we adopt a hierarchical tree-like circuit design philosophy to enhance the model’s parallel computing capabilities while reducing dependence on quantum bit counts and circuit depth. Moreover, the inherent noise resilience in variational quantum algorithms makes this model more suitable for spatiotemporal sequence modeling tasks on NISQ devices. Full article
(This article belongs to the Special Issue Quantum Information Processing and Machine Learning)
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18 pages, 2186 KiB  
Article
Hybrid Quantum Technologies for Quantum Support Vector Machines
by Filippo Orazi, Simone Gasperini, Stefano Lodi and Claudio Sartori
Information 2024, 15(2), 72; https://doi.org/10.3390/info15020072 - 25 Jan 2024
Cited by 6 | Viewed by 3866
Abstract
Quantum computing has rapidly gained prominence for its unprecedented computational efficiency in solving specific problems when compared to classical computing counterparts. This surge in attention is particularly pronounced in the realm of quantum machine learning (QML) following a classical trend. Here we start [...] Read more.
Quantum computing has rapidly gained prominence for its unprecedented computational efficiency in solving specific problems when compared to classical computing counterparts. This surge in attention is particularly pronounced in the realm of quantum machine learning (QML) following a classical trend. Here we start with a comprehensive overview of the current state-of-the-art in Quantum Support Vector Machines (QSVMs). Subsequently, we analyze the limitations inherent in both annealing and gate-based techniques. To address these identified weaknesses, we propose a novel hybrid methodology that integrates aspects of both techniques, thereby mitigating several individual drawbacks while keeping the advantages. We provide a detailed presentation of the two components of our hybrid models, accompanied by the presentation of experimental results that corroborate the efficacy of the proposed architecture. These results pave the way for a more integrated paradigm in quantum machine learning and quantum computing at large, transcending traditional compartmentalization. Full article
(This article belongs to the Special Issue Quantum Information Processing and Machine Learning)
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14 pages, 1632 KiB  
Article
Engineering Four-Qubit Fuel States for Protecting Quantum Thermalization Machine from Decoherence
by Fatih Ozaydin, Ramita Sarkar, Veysel Bayrakci, Cihan Bayındır, Azmi Ali Altintas and Özgür E. Müstecaplıoğlu
Information 2024, 15(1), 35; https://doi.org/10.3390/info15010035 - 10 Jan 2024
Cited by 3 | Viewed by 2329
Abstract
Decoherence is a major issue in quantum information processing, degrading the performance of tasks or even precluding them. Quantum error-correcting codes, creating decoherence-free subspaces, and the quantum Zeno effect are among the major means for protecting quantum systems from decoherence. Increasing the number [...] Read more.
Decoherence is a major issue in quantum information processing, degrading the performance of tasks or even precluding them. Quantum error-correcting codes, creating decoherence-free subspaces, and the quantum Zeno effect are among the major means for protecting quantum systems from decoherence. Increasing the number of qubits of a quantum system to be utilized in a quantum information task as a resource expands the quantum state space. This creates the opportunity to engineer the quantum state of the system in a way that improves the performance of the task and even to protect the system against decoherence. Here, we consider a quantum thermalization machine and four-qubit atomic states as its resource. Taking into account the realistic conditions such as cavity loss and atomic decoherence due to ambient temperature, we design a quantum state for the atomic resource as a classical mixture of Dicke and W states. We show that using the mixture probability as the control parameter, the negative effects of the inevitable decoherence on the machine performance almost vanish. Our work paves the way for optimizing resource systems consisting of a higher number of atoms. Full article
(This article belongs to the Special Issue Quantum Information Processing and Machine Learning)
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Review

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25 pages, 3088 KiB  
Review
Quantum Computing and Machine Learning on an Integrated Photonics Platform
by Huihui Zhu, Hexiang Lin, Shaojun Wu, Wei Luo, Hui Zhang, Yuancheng Zhan, Xiaoting Wang, Aiqun Liu and Leong Chuan Kwek
Information 2024, 15(2), 95; https://doi.org/10.3390/info15020095 - 7 Feb 2024
Cited by 2 | Viewed by 5933
Abstract
Integrated photonic chips leverage the recent developments in integrated circuit technology, along with the control and manipulation of light signals, to realize the integration of multiple optical components onto a single chip. By exploiting the power of light, integrated photonic chips offer numerous [...] Read more.
Integrated photonic chips leverage the recent developments in integrated circuit technology, along with the control and manipulation of light signals, to realize the integration of multiple optical components onto a single chip. By exploiting the power of light, integrated photonic chips offer numerous advantages over traditional optical and electronic systems, including miniaturization, high-speed data processing and improved energy efficiency. In this review, we survey the current status of quantum computation, optical neural networks and the realization of some algorithms on integrated optical chips. Full article
(This article belongs to the Special Issue Quantum Information Processing and Machine Learning)
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23 pages, 860 KiB  
Review
A Survey of Machine Learning Assisted Continuous-Variable Quantum Key Distribution
by Nathan K. Long, Robert Malaney and Kenneth J. Grant
Information 2023, 14(10), 553; https://doi.org/10.3390/info14100553 - 10 Oct 2023
Cited by 4 | Viewed by 3367
Abstract
Continuous-variable quantum key distribution (CV-QKD) shows potential for the rapid development of an information-theoretic secure global communication network; however, the complexities of CV-QKD implementation remain a restrictive factor. Machine learning (ML) has recently shown promise in alleviating these complexities. ML has been applied [...] Read more.
Continuous-variable quantum key distribution (CV-QKD) shows potential for the rapid development of an information-theoretic secure global communication network; however, the complexities of CV-QKD implementation remain a restrictive factor. Machine learning (ML) has recently shown promise in alleviating these complexities. ML has been applied to almost every stage of CV-QKD protocols, including ML-assisted phase error estimation, excess noise estimation, state discrimination, parameter estimation and optimization, key sifting, information reconciliation, and key rate estimation. This survey provides a comprehensive analysis of the current literature on ML-assisted CV-QKD. In addition, the survey compares the ML algorithms assisting CV-QKD with the traditional algorithms they aim to augment, as well as providing recommendations for future directions for ML-assisted CV-QKD research. Full article
(This article belongs to the Special Issue Quantum Information Processing and Machine Learning)
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