IET Quantum Communication最新文献

The problem of selecting an appropriate number of features in supervised learning problems is investigated. Starting with common methods in machine learning, the feature selection task is treated as a quadratic unconstrained optimisation problem (QUBO), which can be tackled with classical numerical methods as well as within a quantum computing framework. The different results in small problem instances are compared. According to the results of the authors’ study, whether the QUBO method outperforms other feature selection methods depends on the data set. In an extension to a larger data set with 27 features, the authors compare the convergence behaviour of the QUBO methods via quantum computing with classical stochastic optimisation methods. Due to persisting error rates, the classical stochastic optimisation methods are still superior.

Ensemble methods aggregate predictions from multiple models, typically demonstrating improved accuracy and reduced variance compared to individual classifiers. However, they often come with significant memory usage and computational time requirements. A novel quantum algorithm that leverages quantum superposition, entanglement, and interference to construct an ensemble of classification models using bagging as an aggregation strategy is introduced. Through the generation of numerous quantum trajectories in superposition, the authors achieve B transformations of the training set with only $��\log �\left(B\right)�$ operations, allowing an exponential enlargement of the ensemble size while linearly increasing the depth of the corresponding circuit. Moreover, when assessing the algorithm's overall cost, the authors demonstrate that the training of a single weak classifier contributes additively to the overall time complexity, as opposed to the multiplicative impact commonly observed in classical ensemble methods. To illustrate the efficacy of the authors’ approach, experiments on reduced real-world datasets utilising the IBM qiskit environment to demonstrate the functionality and performance of the proposed algorithm are introduced.

Ensemble methods aggregate predictions from multiple models, typically demonstrating improved accuracy and reduced variance compared to individual classifiers. However, they often come with significant memory usage and computational time requirements. A novel quantum algorithm that leverages quantum superposition, entanglement, and interference to construct an ensemble of classification models using bagging as an aggregation strategy is introduced. Through the generation of numerous quantum trajectories in superposition, the authors achieve B transformations of the training set with only $��\log �\left(B\right)�$ operations, allowing an exponential enlargement of the ensemble size while linearly increasing the depth of the corresponding circuit. Moreover, when assessing the algorithm's overall cost, the authors demonstrate that the training of a single weak classifier contributes additively to the overall time complexity, as opposed to the multiplicative impact commonly observed in classical ensemble methods. To illustrate the efficacy of the authors’ approach, experiments on reduced real-world datasets utilising the IBM qiskit environment to demonstrate the functionality and performance of the proposed algorithm are introduced.

Joint Remote State Preparation provides a useful way to securely transfer the known quantum states to the distant nodes. However, the limitation of resources often leads to the quantum channels constructed by distributed entangled pairs being incompatible with the transmitted states. In order to overcome this problem, a novel Joint Remote State Preparation protocol was proposed for transmitting general multi-qudit states over quantum networks, providing a promising pathway to utilise the available Einstein-Podolsky-Rosen (EPR) channels with different levels. Several scenarios under noisy environments were discussed and some properties of the fidelity when transmitting the multi-qudit state were demonstrated. It was demonstrated that both the prepared state and the kind of the noises could restrict the number of the participant nodes. Our scheme leverages the existing quantum resources, which addresses the issue of insufficient entanglement resources. This approach is easily adaptable to other quantum network structures, offering a potential solution for constructing a universal quantum network.

Quantum key distribution (QKD) is one of the major applications of quantum information technology. It can provide ultra-secure key distribution with security guaranteed by the laws of quantum physics. Quantum key distribution is necessary to protect data transmission from quantum computing attacks in future communication networks. The laws of quantum mechanics dictate that as opposed to microwave frequencies, quantum coherence is preserved at room temperatures for terahertz (THz) frequencies. This makes the THz band a promising solution for room-temperature QKD implementation in future wireless communication networks. The authors present the principles of continuous variable QKD (CV-QKD) systems and review the latest developments in the design and analysis of CV-QKD systems operating at microwave and THz frequencies. The authors also discuss how multiple-input multiple-output transmission can be incorporated into the quantum communications framework to improve the secret key rates and increase the coverage distances of the THz CV-QKD system. Furthermore, major hardware challenges that must be surmounted to practically realise THz CV-QKD systems are highlighted.

In QKD-enabled optical networks (QKD-enabled ONs), fragmentation is one of the serious issues which can be mitigated through appropriate management of network resources. Thus, efficient allocation of network resources during routing and resource assignment is important to minimise the impact of time slot fragmentation in the quantum signal channel (QSCh) of QKD-enabled ONs. The authors address the fragmentation problem in the QSCh and propose a new fragmentation-suppressed routing and resource assignment (FS-RRA) approach. To evaluate the performance and to analyse the effect of time slot fragmentation in QSCh of QKD-enabled ONs, the proposed FS-RRA approach is compared with two existing resource assignment approaches, namely, the first-fit (FF) and random-fit (RF) for two different networks. Simulation results show that the proposed approach reduces fragmentation by 2.97% and 6.69% for NSFNET and 1.77% and 5.91% for UBN24 in terms of external fragmentation compared to FF and RF, respectively. Furthermore, the proposed approach reduces blocking by 4.03% and 14.28% for NSFNET and 2.61% and 13.44% for UBN24 and improves resource utilisation up to 3.44% and 5.96% for NSFNET and 3.08% and 7.64% for UBN24 compared to FF and RF, respectively.

Trapped graviton in magnetic seas induces magnetic fields as a function of time in the additional space. The Soon Joe generator made the Graviton set behave as free relativistic quantum particles. The current and voltage generated when the LED was turned on and off were measured five times. Measurements were made in units of 1/1000 of a second, and the measured data were summed. In the LED off-state, the average current was −2.87E-03 (A), and the average voltage was −1.44E-01 (V) in VH and 6.83E-01 (V) in VL. The average current in the LED on-stage was −4.28E-03, the VH was 2.14E-01, and the VL was 6.57E-01. The voltage difference was −8.27E-01 in the off-stage and −8.71E-01 in the on-stage. Less current was generated in the off-stage, with less voltage difference. In this experiment, we confirmed that the graviton generates the current, and with the photons, more current is generated. This explains why the interactive induction protocol of gravitons or photons can be used to experiment with the magnetic field's ability to communicate or transfer energy with relativistic quantum particles. The gravitoelectric effect explains the photoelectric effect elements, and graviton has induced electricity as a physical entity in the magnetic sea.

The quantum communication channel is considered to be eavesdropped when the signal Quantum Bit Error Rate (QBER) exceeds a defined theoretical limit and is thus considered a figure of merit parameter for assessing the security of a quantum channel. This work presents a general mathematical model considering device imperfections and various sources of errors for estimating signal QBER in polarisation encoded satellite-based QKD systems. QBER performance for satellite-to-ground downlink scenarios has been investigated for multiple sky brightness conditions (day time and night time operations), two operating wavebands: 800 and 1550 nm as well as for different quantum transmitter and quantum receiver architectures. Further, a novel QBER estimation analysis for inter-satellite QKD links has also been presented. The estimation results obtained from the developed model have been validated against and found in good agreement with the measured results of the only reported satellite-to-ground QKD experiments till date. The presented QBER modelling and analysis will aid in system engineering and efficient design of future satellite-based QKD systems.

The medical information system is rich in datasets, but no intelligent systems can easily analyse the disease. Recently, ML (Machine Learning)-based algorithms have acted as a handy diagnostic tool to identify whether a person is affected by thyroid or not. However, they produced classification with low accuracy and led to misclassification. Hence, the proposed system combines quantum computing with ML techniques to enhance computational power and precision. The system employs modified QPSO (Quantum Particle Swarm Optimisation) for feature selection since its searching performance is better than that of conventional PSO for selecting the optimum global position of the particle, thus selecting the relevant feature. Whereas, the QSVM (Quantum Support Vector Machine) is implemented for more accurate classification than classical SVM, as it tends to capture complex patterns in data produced due to high dimensional feature space applied by quantum kernel functions. This combination of modified QPSO and QSVM tends to increase the performance accuracy significantly. The efficiency of the proposed model is measured based on derivative parameters, such as F-1-score, recall, precision and accuracy, with corresponding confusion matrix and ROC. Further, the classification is compared with other traditional approaches to predict the accuracy of the proposed model with traditional methods.

The performance analysis of a quantum random number generator (QRNG), operating based on the interarrival time differences between consecutive photon detections from a coherent light source, is presented. The proposed analysis approach accurately takes into account the physical properties of the single-photon detection systems, such as discretised time measurement, the correlations induced by the asynchronous arrival of photons with respect to the time resolution grid, and the dead time after observations and provides the QRNG's relevant performance measures, such as the joint distribution of bits, lag-r correlations, the bit generation overhead and the bit generation time. Analysis results are verified by computer simulations.