IET Quantum Communication最新文献

First of all, we want to thank all of our readers, authors and reviewers on behalf of the editing team behind IET Quantum Communication for your support ever since the creation of this journal in 2019.

Quantum computing and deep learning have recently gained popularity across various industries, promising revolutionary advancements. The authors introduce QC-PCSANN-CHIO-FD, a novel approach that enhances fault detection in electrical power systems by combining quantum computing, deep learning, and optimisation algorithms. The network, based on a Pyramidal Convolution Shuffle Attention Neural Network (PCSANN) optimised with the Coronavirus Herd Immunity Optimiser, shows promising results. Initially, historical datasets are used for fault detection. Preprocessing, which includes handling missing data and outliers using Adaptive Variational Bayesian Filtering is followed by Dual-Domain Feature Extraction to extract grayscale statistical features. These features are processed by PCSANN to detect faults. The Coronavirus Herd Immunity Optimisation Algorithm is proposed to optimise PCSANN for precise fault detection. Performance of the proposed QC-PCSANN-CHIO-FD approach attains 24.11%, 28.56% and 22.73% high specificity, 21.89%, 23.04% and 9.51% lower computation Time, 25.289%, 15.35% and 19.91% higher ROC and 8.65%, 13.8%, and 7.15% higher Accuracy compared with existing methods, such as combining deep learning based on quantum computing for electrical power system malfunction diagnosis (QC-ANN-FD), electrical power system fault diagnostics using hybrid quantum-classical deep learning (QC-CRBM-FD), applications of machine learning to the identification of power system faults: Recent developments and future directions (QC-RF-FD).

The integration of lattice-based cryptography principles with Quantum Key Distribution (QKD) protocols is explored to enhance security in the context of Internet of Things (IoT) ecosystems. With the advent of quantum computing, traditional cryptographic methods are increasingly susceptible to attacks, necessitating the development of quantum-resistant approaches. By leveraging the inherent resilience of lattice-based cryptography, a synergistic fusion with QKD is proposed to establish secure and robust communication channels among IoT devices. Through comprehensive Qiskit simulations and theoretical analysis, the feasibility, security guarantees, and performance implications of this novel hybrid approach are thoroughly investigated. The findings not only demonstrate the efficacy of lattice-based QKD in mitigating quantum threats, but also highlight its potential to fortify IoT communications against emerging security challenges. Moreover, the authors provide valuable insights into the practical implementation considerations and scalability aspects of this fusion approach. This research contributes to advancing the understanding of quantum-resistant cryptography for IoT applications and paves the way for further exploration and development in this critical domain.

Quantum calculi and formalisms are useful tools for ensuring security and computational capabilities in blockchain and cryptography. They aid in designing and analysing new cryptographic protocols for blockchain, determining the behaviour of quantum operations in blockchain-based smart contracts, assessing the feasibility and security of quantum algorithms in blockchain applications, and building a quantum-safe blockchain system. A comprehensive review of the applications of quantum calculi and formalisms in computer security and network security, along with a bibliographic analysis is presented. It is unique in that it combines bibliometric analyses with a technical review of the domain of quantum calculi and formalism. Bibliometric and biographic analysis in the field helps identify research trends, assess the influence of research, determine collaboration patterns, evaluate journals, and examine publication behaviours, among other things. It performs bibliographic and bibliometric analysis using a dataset collected from Scopus and Web of Science through different queries. The obtained results help identify important institutions, authors, organisations, collaboration networks, keywords, and more. The provided open challenges and future vision pave the way for further research in the direction of quantum calculi and formalism applications in computer security and network security.

Quantum technology harnesses the principles of quantum mechanics to accomplish tasks in a different way as compared to the classical technologies. This includes quantum computing, which uses qubits for parallel information processing, greatly enhancing computation speed and entanglement and empowering problem-solving abilities. Quantum communication provides secure data transmission through quantum cryptography, while quantum sensing offers improved measurement precision, benefiting areas such as cryptography, material science, and pharmaceuticals. Additionally, the implementation and commercialisation of quantum technology involve transitioning theoretical quantum concepts into practical applications and marketable products. To achieve widespread adoption of quantum industry, significant research efforts are crucial among academia, industry, and government.

The reconciliation method for continuous variable quantum key distribution systems is usually chosen based on its reconciliation efficiency. Nonetheless, one must also consider the requirements of each reconciliation method in terms of the amount of information transmitted on the classical channel. Such may limit the achievable key rates. For instance, multidimensional reconciliation of dimension 8 demands a classical channel bandwidth 43 times greater than that of the quantum channel baud rate. Decreasing the dimension to 4 halves the required bandwidth, allowing for higher quantum channel baud rates and higher key rates for shorter transmission distances, despite the lesser reconciliation performance.

Quantum computing is a radical new paradigm for a technology that is capable to revolutionise information processing. Simulators of universal quantum computer are important for understanding the basic principles and operations of the current noisy intermediate-scale quantum processors, and for building in future fault-tolerant quantum computers. As next-generation quantum technologies continue to advance, it is crucial to address the impact on education and training in quantum physics. The emergence of new industries driven by progress in quantum computing and simulation will create a demand for a specialised quantum workforce. In response to these challenges, the authors present Psitrum, an open-source simulator for universal quantum computers. Psitrum serves as a powerful educational and research tool, enabling a diverse range of stakeholders to understand the fundamental principles and operations of quantum systems. By offering a comprehensive platform for emulating and debugging quantum algorithms through quantum circuits, Psitrum aids in the exploration and analysis of various quantum applications using both MATLAB and MATLAB application programming interface to use the software on other platforms. Psitrum software and source codes are fully available at GitHub.

Quantum machine learning (QML) can be employed in solving complicated machine learning tasks although the performance in examining the regression processes is only barely understood. Knowledge gaps are intended to be closed by studying modelling performance of QML in regression tasks, with emphasis being dedicated to scaling up and ability to resist noise. The regression part offers the following functions that include straight line and complex operations. Furthermore, the authors employ quantum neural networks generated using Qiskit to perform experiments. The results demonstrate that QML has a remarkable level of accuracy in basic regressions, reaching a maximum of 97%. Nevertheless, there are difficulties in representing intricate functions, such as 5 × cos(x), which results in a noticeable decline in performance. The work deals with the influence of noise and IERs from imperfect hardware on the efficiency of QML algorithms providing insight into the core obstacles. The result of a detailed examination of the results that have tested the powers and limits of QML in the development of regression applications is represented. The future direction of research and development will be defined by the results obtained in it.

Drug discovery has become a main challenge in the society, following the COVID-19 pandemic. However, pharmaceutical companies are already using computing to accelerate drug discovery and are increasingly interested in quantum computing (QC), with a view to improving the speed of development process for new drugs. The authors propose a quantum method for generating random sequences based on occurrence in a protein database and quantum algorithms for calculating a similarity rate between proteins. Both concepts can be used for structure prediction in drug design. The aim is to find the proteins closest to the generated protein and obtain an ordering of these proteins. First, the authors will present the construction of a quantum protein generator that defines a protein, called a test protein. The authors will then describe different methods to compute the similarity's rate between each protein in the database and the test protein or, for a case study, the elafin. The algorithms have been extended or adapted to a quantum formalism for use cases, that is, amino acid sequences, and tested to see the added value of quantum versions. The interest is to observe whether QC can be used in the drug discovery process.

Arecanut X-ray images accurately represent their internal structure. A comparative analysis of transfer learning-based classification, employing both a traditional convolutional neural network (CNN) and an advanced quantum convolutional neural network (QCNN) approach is conducted. The investigation explores various transfer learning models with different sizes to identify the most suitable one for achieving enhanced accuracy. The Shufflenet model with a scale factor of 2.0 attains the highest classification accuracy of 97.72% using the QCNN approach, with a model size of 28.40 MB. Out of the 12 transfer learning models tested, 9 exhibit improved classification accuracy when using QCNN models compared to the traditional CNN-based transfer learning approach. Consequently, the exploration of CNN and QCNN-based classification reveals that QCNN outperforms traditional CNN models in accuracy within the transfer learning framework. Further experiments with qubits suggest that utilising 4 qubits is optimal for classification operations in this context.