IET Quantum Communication最新文献

Blockchain technology is a highly developed database system that shares information within a business web. It stores details in blocks connected chronologically, ensuring information integrity through consensus mechanisms that prevent unauthorised alterations. This decentralised system removes the need for a believable mediator, mitigating vulnerabilities and enhancing transaction security. Blockchain’s application spans the energy, finance, media, entertainment, and retail sectors. However, classical blockchain faces threats from quantum computing advancements, necessitating the development of quantum blockchain technology. Quantum blockchain, leveraging quantum computation and information theory, offers enhanced security and immutability. In this paper, different mathematical foundations, practical implementations and effectiveness of lattice-based cryptography in securing blockchain applications are discussed. Analysis of how the cryptographic techniques can protect blockchain systems against quantum attacks is being done by using mathematical formulations and examples. Quantum computing strengthens blockchain security with advanced encryption and authentication, which is critical for safeguarding diverse sectors from evolving cyber threats. Further study on quantum-resistant design is necessary if blockchain networks are to be robust and intact in the face of future technological developments.

Quantum-proof randomness extraction is essential for handling quantum side information possessed by a quantum adversary, which is widely applied in various quantum cryptography tasks. In this study, the authors introduce a real-time two-source quantum randomness extractor against quantum side information. The authors’ extractor is tailored for forward block sources, a novel category of min-entropy sources introduced in this work. These sources retain the flexibility to accommodate a broad range of quantum random number generators. The authors’ online algorithms demonstrate the extraction of a constant fraction of min-entropy from two infinitely long independent forward block sources. Moreover, the authors’ extractor is inherently block-wise parallelisable, presenting a practical and efficient solution for the timely extraction of high-quality randomness. Applying the authors’ extractors to the raw data of one of the most commonly used quantum random number generators, a simulated extraction speed as high as 64 Gbps is achieved.

As a special voting method, one-vote veto voting also has a wide range of applications. A veto means that when the voting council puts forward a proposal, it cannot pass unless all the voters agree to it. If there is a no vote, the proposal will be rejected, but no one will know how anyone else votes. In most existing quantum anonymous one-vote veto voting protocols, an absolutely honest third party is generally required to assist the voting. However, it is difficult to find a fully trusted third party in reality. In addition, the existing quantum anonymous one-vote veto protocol does not consider the attack from the insider voters. Therefore, based on the characteristics of entanglement swapping between the Cat state and Bell state, the authors propose a new quantum anonymous one-vote veto protocol, which can not only calculate the voting result quickly and effectively but also demonstrate higher security.

Machine learning has emerged as a promising method for predicting breast cancer using quantum computation techniques. Quantum machine learning algorithms, such as quantum support vector machines (QSVMs), are demonstrating superior efficiency and economy in tackling complex problems compared to traditional machine learning methods. When compared with classical support vector machine, the quantum machine produces remarkably accurate results. The suggested quantum SVM model in this study effectively resolved the binary classification problem for diagnosing malignant breast cancer. This work introduces an enhanced approach to breast cancer diagnosis by integrating QSVM with elitist non-dominated sorting genetic optimization (ENSGA), leveraging the strengths of both techniques to achieve more accurate and efficient classification results. ENSGA plays a crucial role in optimising QSVM parameters, ensuring that the model attains the best possible classification accuracy while considering multiple objectives simultaneously. Moreover, the quantum kernel estimation method demonstrated exceptional performance by achieving high accuracy within an impressive execution time of 0.14 in the IBM QSVM simulator. The seamless integration of quantum computation techniques with optimisation strategies such as ENSGA highlights the potential of quantum machine learning in revolutionising the field of healthcare, particularly in the domain of breast cancer diagnosis.

This paper introduces the parallel quantum transfer fractal priority reply with dynamic memory (P-QTFPR-DM) algorithm, an innovative approach that combines quantum computing and reinforcement learning (RL) to enhance decision-making in autonomous vehicles. Leveraging quantum principles such as superposition and entanglement, P-QTFPR-DM optimises Q-value approximation through a custom quantum circuit (UQC), facilitating efficient exploration and exploitation in high-dimensional state-action spaces. This algorithm utilises a quantum neural network (QNN) with 4 qubits to encode and process Q-values. The autonomous vehicle, equipped with GPS for real-time navigation, uses P-QTFPR-DM to reach a predefined destination with coordinates 12.82,514,234,148 latitude and 80.0,451,311,962,242 longitude. Through extensive numerical simulations, P-QTFPR-DM demonstrates a 30% reduction in decision-making time and a 25% improvement in navigation accuracy compared to classical RL methods. The QNN-based approach achieves a 95% success rate in reaching the destination within a 5-m accuracy threshold, whereas traditional RL methods achieve only an 85% success rate. Dynamic memory management in P-QTFPR-DM optimises computational resources, enhancing the vehicle's adaptability to environmental changes. These results highlight the potential of quantum computing to significantly advance autonomous vehicle technology by improving both efficiency and effectiveness in complex navigation tasks. Future research will focus on refining the algorithm and exploring its real-world applications to enhance autonomous vehicle performance.

The authors present a novel token exchange scheme with an example of an electronic cash (eCash) transaction scheme that ensures quantum security, addressing the vulnerabilities of existing models in the face of quantum computing threats. The authors’ comprehensive analysis of various quantum blind signature mechanisms revealed significant shortcomings in their applicability to eCash transactions and their resilience against quantum adversaries. In response, the authors drew inspiration from D. Chaum's original classical eCash scheme and innovated a quantum-secure transaction framework. The authors detail the developed protocol and rigorously evaluate its security aspects. The protocol's adherence to critical security requirements such as blindness, non-forgeability, non-deniability, and prevention of double spending is analysed. Moreover, the scheme against Intercept and Resend, Denial of Service, Man-in-the-Middle, and Entangle-and-Measure attacks is rigorously tested. The authors’ findings indicate a robust eCash transaction model capable of withstanding the challenges posed by quantum computing advancements.

Designing optimal measurement operators for quantum state discrimination (QSD) is an important problem in quantum communications and cryptography applications. Prior works have demonstrated that optimal quantum measurement operators can be obtained by solving a convex semidefinite program (SDP). However, solving the SDP can represent a high computational burden for many real-time quantum communication systems. To address this issue, a majorisation-minimisation (MM)-based algorithm, called Quantum Majorisation-Minimisation (QMM) is proposed for solving the QSD problem. In QMM, the authors reparametrise the original objective, then tightly upper-bound it at any given iterate, and obtain the next iterate as a closed-form solution to the upper-bound minimisation problem. Our numerical simulations demonstrate that the proposed QMM algorithm significantly outperforms the state-of-the-art SDP algorithm in terms of speed, while maintaining comparable performance for solving QSD problems in quantum communication applications.