High-Energy Quantum Entanglement Imaging: A Critical Review

Manuel Erhard, Mario Krenn, Anton Zeilinger

Jaleh Tamangdorji

The rapid advancement of Wireless Sensor Networks (WSNs) and Internet of Things (IoT) technologies has transformed modern healthcare by enabling continuous acquisition, transmission, and intelligent analysis of medical data. In medical image diagnostics, these technologies support remote monitoring, early disease identification, and timely clinical decision-making. However, transmitting and processing high-dimensional medical images through resource-constrained WSN-assisted IoT environments introduces challenges related to energy consumption, bandwidth limitations, computational complexity, and image-quality preservation. Conventional Convolutional Neural Networks (CNNs) provide strong diagnostic performance but often demand substantial computational resources, limiting their suitability for energy-constrained healthcare devices. Quantum Convolutional Neural Networks (QCNNs) have consequently emerged as a promising computational paradigm for efficient medical image analysis. By exploiting quantum principles such as superposition and entanglement, QCNNs offer opportunities for efficient feature representation and reduced computational overhead. Integrating attention mechanisms can further strengthen diagnostic performance by emphasizing clinically significant image regions while suppressing irrelevant information. This review examines energy-efficient QCNNs integrated with attention-based models for quality-preserving medical image diagnostics in WSN-assisted IoT environments. It critically evaluates recent architectures, energy-efficiency strategies, diagnostic performance, and quality-preservation methods while identifying existing limitations and research gaps. The review further highlights future directions toward scalable, intelligent, and resource-efficient IoT healthcare diagnostic frameworks.

Sakar Parajuli

Deepak Kumar, Sushil K. Sharma, Paweł Moskal

Abstract Quantum electrodynamics (QED) predicts that photons from positronium decay are maximally entangled. However, experimental verification of this entanglement in high-energy photons has remained elusive due to competing decay processes and interactions with the surrounding medium. The J-PET detector is a multi-modules detector based on plastic scintillators that has the potential to measure such correlation in the whole phase space. Photons interact with plastic mainly via Compton scattering, which makes J-PET particularly suitable for this type of study. Moreover, by incorporating the positron lifetime as an additional parameter, to differentiate between various decay processes and examine how the polarization correlation changes as a function of lifetime. Such studies provide new insights into quantum entanglement at high energy regimes and hold potential to enhance positron emission tomography (PET) imaging techniques. Recent results demonstrate the J-PET detector’s capability to measure the polarization correlation of photons emitted from positron annihilation in a porous medium. In this paper, we present a review of past research in this field, highlighting the need for further investigation of polarization correlation of annihilation photons.

Pyarali Somanathan

Wireless Sensor Network (WSN)-assisted Internet of Things (IoT) systems have transformed modern healthcare by enabling continuous patient monitoring, remote diagnostics, and real-time medical image transmission. However, limited sensor resources, bandwidth constraints, and noise interference create significant challenges in maintaining energy efficiency and preserving diagnostic image quality. Recent advances in Artificial Intelligence (AI), particularly Quantum Convolutional Neural Networks (QCNNs) integrated with attention-based models, have emerged as promising solutions for addressing these challenges. QCNNs exploit quantum computing principles, including superposition and entanglement, to process high-dimensional medical images with reduced computational complexity and improved feature extraction compared with conventional convolutional neural networks. Furthermore, hybrid quantum-classical architectures enhance learning efficiency and achieve high diagnostic performance even with limited training data. Attention mechanisms complement QCNNs by emphasizing clinically relevant image regions, suppressing noise, and improving feature representation for classification, segmentation, and disease detection. Their integration significantly enhances diagnostic accuracy, transmission efficiency, and energy conservation in resource-constrained WSN environments. This systematic review comprehensively examines recent developments in QCNN and attention-based medical image diagnostics, compares existing methodologies, identifies current research challenges, and outlines future directions for developing scalable, energy-efficient, and intelligent IoT-enabled healthcare systems with improved clinical decision support.

Shigenori Seki