ABSTRACT Classical deep learning achieves high accuracy in image analysis tasks but require large volume of data for generalisation. Medical image datasets are often small and expensive to annotate. Arguably, quantum machine learning (QML) has the following benefits over classical machine learning (ML). (i) Quantum superposition and entanglement allow quantum machines to excel over the computational competence of classical computers. (ii) By parallel processing, QML solves problems faster. (iii) QML produces promising results with limited‐size image datasets and limited‐parameter circuits. The practical advantages of QML over classical methods are still emerging and not consistent across all application domains. Ongoing research in quantum hardware and algorithms are expected to bridge this gap. In this review, we provide an outline of quantum neural networks, quantum convolution neural networks and various hybrid models characterised by continuous learning. We also explore human brain‐inspired quantum neuromorphic computing using quantum spiking neural networks, characterised by learning from discrete neuromorphic spikes. We discuss quantum circuits, used for different medical image applications, from the perspectives of the circuit topology, the numbers of input and measurement qubits and rotation and entanglement gates. Furthermore, we conducted a systematic review of the literature on QML‐based medical image applications, datasets and benchmarks, and the analysis of the research gap separately indicating possible improvement opportunities.

ABSTRACT Quantum cryptography has increasingly attracted interest from both industry and academia for its potential in real‐world applications. With advancements in quantum electronics, practical quantum devices are now commercially available and poised for broader adoption. Quantum key distribution (QKD) leverages fundamental principles of quantum mechanics to ensure secure communication, whereas quantum key distribution networks (QKDN) serve as the foundational infrastructure for deploying and scaling these secure systems. This paper examines the design methodologies specific to QKD within QKDN, emphasising both the conceptual framework and the practical challenges inherent in the field. In the pursuit of developing robust QKDN design, several key obstacles, related to the foundational QKD technology, must be addressed. These include decoherence, which affects the stability of quantum states and reduces key generation rates; latency, which disrupts synchronisation throughout the QKD network; and intrinsic quantum noise, an inherent property of quantum systems that primarily limits the overall performance of the QKD network. In addition, the limited communication ranges and the requirement for highly specialised hardware complicate the practical deployment of these networks. This work serves as an introductory guide for researchers entering the field, providing an overview of the fundamental principles of QKD network design and the distinctive routing characteristics inherent to such systems. It outlines the essential steps and considerations for building efficient and resilient QKDN infrastructures, including a discussion of the foundational principles, protocols and existing challenges.

ABSTRACT In this paper, we explore continuous‐time quantum walk on homogeneous tree. By applying the stratification method, we treat vertices in the same stratum of a homogeneous tree as a single vertex and obtain the probability amplitudes for different strata in finite homogeneous tree parts. In particular, when (where denotes the number of strata), we derive the probability amplitudes for each stratum of the homogeneous tree, considering the homogeneous tree's transition from a finite to infinite structure.

ABSTRACTThis 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.

ABSTRACT Medical image integrity is critical as telemedicine, cloud PACS and AI‐assisted diagnostics become routine. We present a tamper localisation framework that embeds authentication signatures in the phase domain of blockwise quantum Fourier transform (QFT) coefficients. The watermark is phase‐only, energy preserving and keyed through sparse midband supports with paired phase differences; a light cross‐block coupling imposes spatial consistency so that localised edits produce coherent high‐contrast residuals confined to manipulated regions after inverse QFT. Because magnitudes remain unaltered, benign photometric variations are naturally attenuated, improving specificity under common acquisition and storage pipelines. The verifier computes circular phase residuals and applies an adaptive threshold to generate blockwise tamper maps, which are refined to pixel resolution. Across standard distortions (JPEG recompression, Gaussian noise and blur) and localised forgeries (copy–move, inpainting and contrast edits), the scheme maintains diagnostic fidelity (typical PSNR 40 dB, SSIM 0.98) while delivering precise spatially resolved detection. The design is deterministic and reproducible via seeded keys, integrates with DICOM workflows and is amenable to future quantum hardware realisation. This work contributes a quantum‐ready, imperceptible and localisation‐oriented approach to medical image authentication suitable for deployment in modern healthcare systems. The proposed QFT phase–only watermark achieves imperceptibility (global PSNR dB; SSIM ) and detects localised tampering (ROC AUC under class imbalance).

ABSTRACT The emergence of quantum computing has introduced unprecedented security challenges to conventional cryptographic systems, particularly in the domain of classical communications. Our research addresses these challenges by creatively combining quantum key distribution (QKD), specifically the E91 protocol, with logistic chaotic maps to establish a secure image transmission scheme. Our approach utilises the pseudo‐randomness of chaotic systems alongside the security mechanisms inherent in quantum entanglement‐based protocols. This framework leverages the E91 protocol for secure quantum key distribution to generate identical key pairs at both ends, followed by chaos encryption using the key as a basis for the parameters. This framework utilises the E91 protocol for secure quantum key distribution, leveraging maximally entangled pairs and CHSH inequality tests to detect eavesdropping and potential double‐agent attacks by identifying nonentangled qubits, therefore maintaining key confidentiality. Furthermore, through quantitative simulations, we demonstrate the effectiveness of this scheme through key space and key sensitivity analysis, histogram analysis, information entropy analysis, execution time analysis, and differential attack analysis in end‐to‐end encryption. The results indicate a significant improvement in encryption and decryption efficiency, showcasing the scheme's potential as a viable solution against the vulnerabilities posed by quantum computing advancements. Our research offers a novel perspective on a critical aspect of cybersecurity applications across healthcare, defence, finance, and beyond in the realm of secure quantum communication.

ABSTRACT Power quality disturbances (PQDs) pose significant challenges to modern power systems, necessitating precise detection and identification to mitigate their impacts and enhance grid robustness. In this paper, we propose a hybrid quantum‐classical convolutional neural network model (PQDs‐QC‐CNN) for detecting and identifying power quality disturbances with high efficiency. The model employs a hierarchical framework consisting of quantum convolutional layers, fully connected layers and softmax regression, which can effectively extract multiscale features from disturbance data while mitigating overfitting. Utilising quantum bits, the model achieves a time complexity of and a space complexity of , ensuring scalability and efficiency. By conducting experiments on the datasets generated in compliance with IEEE Std 1159–2019, the results show a 100% detection accuracy and 99.56% identification accuracy, even with minimal quantum bits and simple configurations. Additionally, the model demonstrates robust noise resistance, maintaining approximately 98% identification accuracy across various noise scenarios. PQDs‐QC‐CNN not only shows promise for power system applications but also explores new avenues for quantum algorithm integration in smart grid technologies.

Abstract 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.

Abstract 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.