IET Quantum Communication最新文献

This study presents the challenges of learning deformable offsets in conventional machine learning (ML) systems. It significantly focuses on the representation data derived from the MNIST and FashionMNIST datasets. The primary difficulty with this approach is optimising a trade-off between accuracy and efficiency by exploiting the gradient-based algorithm. It is a significant phase of the image recognition and transformation process. Provide a strategy for incorporating quantum approaches utilising quantum loss functions, entanglement, and quantum feature maps to improve on conventional gradient-based techniques. Employ hybrid ways that combine quantum algorithms, such as quantum natural gradient descent (QNGD) and variational quantum eigensolver (VQE), with classical optimisation techniques. This approach is applied to updating deformable offsets and optimising quantum eigenvalue issues. We use quantum Fisher information matrices (FIM) and train tensor networks efficiently and accurately. Then, we performed extensive tests comparing the quantum method with established conventional baselines through hyperparameters, such as accuracy, precision, recall and F1 score. The implementation results demonstrate significant gains in classification accuracy, which exhibit 97% on the MNIST dataset and 87% on the FashionMNIST dataset. The result of the paper emphasises significant conclusions, including improved model stability, increased generalisability and decreased overfitting, due to implementing quantum optimisation techniques. With quantum principles applied to convolution and feature extraction, such data exhibit substantial potential in processing.

In this paper, we introduce an improved spontaneous Raman scattering (SRS) model to accurately determine the quantum communication channel's quality when integrated into a Dense Wavelength Division Multiplexing (DWDM) network. To assess the degradational effect of counter-propagating SRS, we carried out laboratory experiments to analyse the impact of different variables, namely the input power, fibre length and channel arrangement. Based on the measurement results, we developed a new model for defining SRS within the C-band, which provides more precision in describing the impact of SRS compared to the previously used simplified V-shape model. A 96-channel DWDM use case, including one quantum and 90 classical channels, is modelled to identify the optimal channel wavelength allocation strategy. Using the revised model, we concluded that the optimal channel layout, where the channel numbering is based on the ITU-T standard, is with the quantum channel being the 88th (1533.4 nm), or the 96th (1530.2 nm) if we consider the classical capacity. In contrast, if the V-shape model is used for defining the optimal channel allocation, the quantum channel would be the 59th (1545 nm). The results show the importance of accurately modelling SRS, as determining the right channel placement is essential for the coexistence of quantum and classical channels.

Information reconciliation is a critical component of quantum key distribution, ensuring that mismatches between Alice's and Bob's keys are corrected. In this study, we analyse, simulate, optimise and compare the performance of two prevalent algorithms used for information reconciliation: Cascade and LDPC codes in combination with the blind protocol. We focus on their applicability in practical and industrial settings, operating in realistic and application-close conditions. The results are further validated through evaluation on a live industrial QKD system.

Many quantum algorithms operate on classical data, by first encoding classical data into the quantum domain using quantum data encoding circuits. To be effective for large data sets, encoding circuits that operate on large data sets are required. However, as the size of the data sets increases, the encoding circuits quickly become large, complex and error prone. Errors in the encoding circuit will provide incorrect inputs to quantum algorithms, making them ineffective. To address this problem, a formal method is proposed for verification of encoding circuits. The key idea to address scalability is the use of abstractions that reduce the verification problem to bit-vector space. The major outcome of this work is that using this approach, the authors have been able to verify encoding circuits with up to 8191 qubits with very low memory (85 MB) and time (0.29s), demonstrating that the proposed approach can easily be employed to verify even much larger encoding circuits. The results are very significant because, traditional verification approaches that rely on modelling quantum circuits in Hilbert space have only demonstrated verification scalability up to 250 qubits. Also, this is the first approach to tackle the verification of quantum encoding circuits.

In the Industrial Internet of Things (IIoT) context, heterogeneous IIoT nodes need diverse performance requirements, including throughput and quality of service (QoS). These IIoT nodes transmit data over a common shared communication medium. The existing critical challenge arises in efficiently scheduling access to this shared medium among a large number of connected IIoT nodes. To address the challenge of random access in IIoT networks, the power of the entanglement-assisted (EA) protocol was exploited to expand the capacity region boundaries of the shared communication medium, thereby enhancing the throughput and quality-of-service (QoS) requirements of the heterogeneous IIoT network. In the literature, IIoT networks are mainly categorised into two types: centralised and distributed. In this paper, we proposed two distinct models: (1) a centralised multi-class IIoT network based on EA protocol and (2) a distributed multi-class IIoT network based on EA protocol. Next, the authors analytically demonstrated that integrating the EA protocol into both proposed types of multi-class IIoT networks significantly increases the capacity region boundaries compared to the classical reference model, namely slotted ALOHA (SA). Finally, the network performance boundaries were evaluated by analysing the throughput values for different network classes and varying numbers of IIoT nodes. The results demonstrate that, for both proposed models (1) and (2), the transmitted load generated by the IIoT nodes over the shared medium achieves dramatically higher throughput compared to the reference IIoT network based on SA.

Given the diverse array of physical systems available for quantum computing and the absence of a well-defined quantum Internet protocol stack, the design and optimisation of quantum networking protocols remain largely unexplored. To address this, the authors introduce an approach that facilitates the establishment of paths capable of delivering end-to-end fidelity above a specified threshold, without requiring detailed knowledge of the quantum network's properties. In this study, the authors define algorithms that are specific instances of this approach and evaluate them in comparison to Dijkstra's shortest path algorithm and a fully knowledge-aware algorithm through simulations. The authors’ results demonstrate that one of the proposed algorithms consistently outperforms the other methods in delivering paths above the fidelity threshold, across various network topologies and the number of source-destination pairs involved, while maintaining significant levels of fairness among the users and being robust to inaccurate estimations of the expected end-to-end fidelity.

This study investigates the application of quantum algorithms, specifically the Variational Quantum Eigensolver (VQE) and the Quantum Approximate Optimization Algorithm (QAOA), to design optimal sensor configurations for beamforming, enhancing signal quality and overall system performance. We propose two distinct optimization formulations: one aimed at maximising array gain while the other aimed at maximising signal-to-noise-interference ratio (SINR). Our findings show that the outputs obtained from quantum algorithms are consistent with those derived from classical methods.

High-dimensional quantum states, or ‘qudits’, provide significant advantages over traditional qubits in quantum communication, such as increased information capacity, enhanced noise resilience, and reduced information loss. Despite these benefits, their implementation has been constrained by challenges in generation, transmission, and detection. This paper presents a novel theoretical framework for transmitting quantum information using qudit entanglement distribution over a superposition of causal orders in two quantum channels. Using this model, a quantum switch operation for 2-qudit systems is introduced, which facilitates enhanced fidelity of entanglement distribution and quantum teleportation. The results demonstrate that the use of qudits in entanglement distribution achieves a fidelity improvement from 0.5 (for qubit-based systems) to 0.94 for 20-dimensional qudits, even under noisy channel conditions. This enhancement is achieved by exploiting the increased Hilbert space of high-dimensional states and the inherent noise-resilience properties of quantum switches operating in superpositions of causal orders. The findings underscore the potential of qudit-based quantum systems in achieving robust and high-fidelity communication in environments where traditional qubit-based systems face limitations.

Hyperentangled swapping is a quantum communication technique that involves the exchange of hyperentangled states, which are quantum states entangled in multiple degrees of freedom, to enable secure and efficient quantum information transfer. In this paper, we demonstrate schematics for the hyperentanglement swapping between separate pairs of neutral atoms through the mathematical framework of atomic Bragg diffraction, which is efficient and resistant to decoherence, yielding deterministic results with superior overall fidelity. The utilised cavities are in a superposition state and interact with the incoming atoms off-resonantly. Quantum information carried by the cavities is swapped through resonant interactions with two-level auxiliary atoms. We also discuss entanglement swapping under a delayed-choice scenario and provide a schematic generalisation covering multiple-qubit scenarios. Finally, we introduce specific experimental parameters to demonstrate the experimental feasibility of the scheme.

The evolution of wireless communication systems has led to the emergence of the sixth generation (6G) communication architecture, characterised by transformative technologies and novel paradigms that transcend the capabilities of its predecessors. This paper presents an overview of the various aspects of 6G communication architecture, focusing on its technologies, challenges, and applications within the domain of artificial intelligence (AI). The abstract provides an overview of a review on 6G communication, focusing on its architecture, technologies, challenges, security concerns, and requirements within the context of the AI domain. The paper explores the evolving landscape of wireless communication, delving into the anticipated features and capabilities of 6G networks. The architecture emphasises the integration of AI-driven elements, such as intelligent resource allocation and autonomous network management. Various technologies, including terahertz frequencies and integrated satellite networks, are discussed in terms of their potential to reshape connectivity paradigms. However, alongside the promises, a multitude of challenges arise. These encompass spectrum scarcity at terahertz frequencies, energy efficiency concerns, and the need for global standardisation. Addressing security challenges is crucial, considering the expanded attack surface and the integration of AI-powered functionalities. The paper also delineates the stringent requirements that 6G must fulfil, spanning ultra-low latency, high bandwidth, massive device connectivity, and reliable communication. Contextualising these discussions, the review highlights applications within the AI domain that stand to benefit from 6G advancements. These include edge AI, augmented reality, autonomous systems, and IoT-enabled environments. By synergising cutting-edge wireless capabilities with AI-driven intelligence, 6G is poised to revolutionise industries and societal experiences in unprecedented ways.