Medical image encoding has several challenges. File sizes are large. Processing is slow. Fine details like edges, textures, and soft tissue patterns can be lost. Traditional methods use matrix operations that are powerful but often hard to scale and interpret. These limitations highlight opportunities to improve image encoding with better interpretability. To explore new opportunities for scalable image encoding, we introduce a quantum inspired model built on a dictionary structure. In this model, quantum states are stored as key value pairs for ensuring interpretability and scalability. We do not directly implement quantum gates. we provide dictionary based alternatives that emulate gate behavior using key value logic. We simulate the effect of quantum gates on these structures. For example, a Hadamard like rule may help capture sharp or high contrast areas. Phase like rules (such as$S$and$T$) may respond to smooth or soft changes. Control rules, like$C X$and$C C X$, can connect different parts of the image using logic. It may reduce the number of steps needed for image operations. This makes it useful for small quantum image datasets. It also makes the steps more explainable. The process is also traceable through the dictionary logic. We tested this method on grayscale MRI image patches. It performs well in tasks such as image compression. It also supports pattern recognition and secure image sharing. These results suggest its suitability for medical image applications. The model balances technical performance and explainability. This makes it helpful for developers building quantum tools. It also supports researchers and students in medical image analysis.
Medical image encoding has several challenges. File sizes are large. Processing is slow. Fine details like edges, textures, and soft tissue patterns can be lost. Traditional methods use matrix operations that are powerful but often hard to scale and interpret. These limitations highlight opportunities to improve image encoding with better interpretability. To explore new opportunities for scalable image encoding, we introduce a quantum inspired model built on a dictionary structure. In this model, quantum states are stored as key value pairs for ensuring interpretability and scalability. We do not directly implement quantum gates. we provide dictionary based alternatives that emulate gate behavior using key value logic. We simulate the effect of quantum gates on these structures. For example, a Hadamard like rule may help capture sharp or high contrast areas. Phase like rules (such asSandT) may respond to smooth or soft changes. Control rules, likeCXandCCX, can connect different parts of the image using logic. It may reduce the number of steps needed for image operations. This makes it useful for small quantum image datasets. It also makes the steps more explainable. The process is also traceable through the dictionary logic. We tested this method on grayscale MRI image patches. It performs well in tasks such as image compression. It also supports pattern recognition and secure image sharing. These results suggest its suitability for medical image applications. The model balances technical performance and explainability. This makes it helpful for developers building quantum tools. It also supports researchers and students in medical image analysis.
Quantum image processing uses quantum computing, which implements the concepts of entanglement and superposition to increase efficiency, scalability, and revolutionary promise in demanding image processing ap plications. In this research paper, a comprehensive frame work for quantum image processing (QIP) is presented, which includes innovative quantum algorithms for pattern recognition, image improvement, and representation. The system presents advanced quantum encoding models for effective image data manipulation and storage. In addition, it discusses the quantum image-based algorithms that give better results than classical techniques in terms of speed and accuracy for image enhancement tasks such as edge recognition, contrast adjustment, and noise reduction. Researchers have developed quantum-based feature ex traction and classification methods for pattern recognition, which demonstrate enhanced scalability and accuracy for complex visual data. It has various applications in satellite data processing, secure visual data transmission, and medical imaging highlighted by simulations and theoretical analysis that confirm the proposed approaches.
Abstract Quantum Image Representation (QIR) is a fundamental concept in quantum image processing. It describes how classical images are converted into quantum states for processing on quantum computers. Researchers proposed several QIR models, with FRQI and NEQR being the most popular ones. These models show the possibilities of the quantum image encoding. Nevertheless, they are also limited in some way. Mostly, pixel intensity accuracy, the required number of qubits, and circuit complexity have trade-offs. Their application to Noisy Intermediate-Scale Quantum (NISQ) devices becomes challenging, the quantum resources being small in such devices, due to this. In the following paper, we introduce our own approach, which is the Intensity-Preserving Quantum image Representation (IP-QIR). The key aim of IP-QIR is to maintain the information about the grayscale intensities and decrease the amount of quantum resources. IP-QIR quantifies pixel intensities by a controlled rotation scheme. The measurement statistics of a single qubit hold the intensity information and the position qubits represent the spatial information. The strategy uses small patches of images rather than complete images to make it easy to implement. This will decrease the depth of circuit design and makes the approach less advanced to the near-term quantum hardware. IBM Qiskit simulations are used in assessing the performance of IP-QIR. The three types of grayscale images of which experiments are run are the synthetic image patches, SAR images and medical TB chest X-ray images. It is revealed that IP-QIR maintains the intensity information compared to the FRQI and NEQR. SAR and medical data can reach values of fidelity of 84.12\%. The other notable point is that the size of a 4 x 4 image patch only needs five qubits which are much lower than in NEQR. Meanwhile, good reconstruction accuracy is achieved. These findings demonstrate the fact that IP-QIR is an effective and realistic approach to quantum image representation on the NISQ-era quantum devices.
The ability to effectively store and transmit high-resolution images such as MRI and CT scans without losing quality is critical to modernizing medical imaging. Traditional compression methods risk losing essential medical image data, which requires perfect detail for diagnosis. Quantum algorithms use superposition and entanglement to compress faster while preserving important information. This research presents a Quantum-enhanced Artificial Neural Network (QANN) model that combines quantum feature extraction with classical neural network topologies to improve image compression. Our approach consists of converting standardized classical data into quantum states, controlling these states using parameterized quantum circuits, and measuring the resulting states to produce enhanced feature vectors. The quantum-enhanced features are fed into a traditional neural network for image compression. The experimental results clearly show that our QANN framework outperforms standard models in terms of accurate reconstructed images, reduced size, and increased space-saving percentage, especially when dealing with large and complicated datasets. The QANN model demonstrates how quantum computing can significantly enhance the effectiveness of medical image processing solutions. Kaggle brain CT and MRI datasets and COVID-CXNet chest x-ray images are used. The proposed QANN model improves peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). Using quantum technology, the image size is reduced for MRI (73.3 %), X-ray (74.1%), and CT-SCAN (71.8%) to save space.
Quantum image computing has attracted attention due to its vast storage and faster processing of image data than classical computers. Although classical computing power has grown substantially in the last decade, its abilities have plateaued, struggling to meet the needs of huge datasets. Quantum computers, on the other hand, take advantage of unique qualities like quantum parallelism, superposition, and entanglement to provide faster solutions to complicated problems. Managing huge image data in classical systems necessitates significant memory and hardware resources, which makes quantum computing a possible alternative. Several approaches have arisen for encoding and compressing classical images in the quantum processor. However, one of the most significant limitations is the complexity of quantum state preparation, which translates pixel coordinates to corresponding quantum circuits. Current approaches for representing large-scale images require a huge number of quantum resources including qubit, and qubit connection, which presents significant hurdles. Therefore, this article aims to overview the pixel intensity and state preparation circuit that needs fewer connections, i.e., quantum gates to represent quantum image data, and then explore and design effective compression and processing methods for medium and high-resolution images. This work specializes in emerging quantum models’ potential for enhancing image representation and compression capabilities. It also conducts a comprehensive study of quantum image representation and compression techniques, categorizing existing methods by grayscale and color image types and evaluating their methods, strengths, and limitations. It focuses on crucial difficulties, such as high qubit requirements and complex state preparation, that limit scalability for huge images. This paper also assesses each model’s compression efficacy, which will help future research focus on efficient circuit designs with lower complexity for medium to high-resolution images. It is an essential reference for advancing quantum image processing research since it provides a systematic framework for evaluating quantum image compression and representation algorithms.
A lung cancer record can contain very different kinds of data. A tissue slide shows cells and tissue shape. A blood test gives numbers such as white blood cell count, haemoglobin, albumin, glucose, and creatinine. A urine test gives numbers such as urine albumin and urine creatinine. Many computer models use only one of these data types. We propose Q-MedDict, a quantum-inspired dictionary model for image and laboratory data. The model stores information as key–value pairs. The key is a bit string. The value is an amplitude. Gate-like rules move or mix the amplitudes. The method follows the idea of dictionary-based quantum image encoding, but it adds a second branch for blood and urine data. The image branch was tested on two public lung adenocarcinoma H&E images. The laboratory branch used public NHANES release statistics for blood and urine variables. We did not pair CMB-LCA patients with NHANES people because they are different cohorts. The two branches were compared by the same encoding measures instead. With only 25% of block coefficients, the two lung images kept 99.80% and 99.35% of transform energy. Their SSIM values were 0.919 and 0.910. A 50% dictionary state for blood kept 98.64% of transform energy with cosine similarity 0.993. A 50% urine state kept 98.53% with cosine similarity 0.993. These results show one common pattern: image, blood, and urine information can be placed in the same dictionary form and reduced while most of the encoded signal stays present. The result is a small proof-of concept. It is not a lung cancer diagnostic model. A matched cohort with slides and laboratory tests from the same people is needed for clinical prediction.
Purpose This study investigates the integration of quantum-enhanced machine learning (QML) into medical image analysis, focusing on classification and feature extraction tasks. It aims to evaluate whether hybrid quantum-classical models can overcome the limitations of traditional machine learning in handling high-dimensional, clinically relevant imaging data. Design/methodology/approach Hybrid architectures combining classical preprocessing with quantum components, specifically variational quantum circuits (VQCs) and quantum kernel methods, were developed and implemented in simulated quantum environments using Qiskit and PennyLane on Google Colab. Publicly available datasets (ChestX-ray14, Brain Tumor MRI and CT Hemorrhage) were preprocessed and encoded into quantum states. Model performance was evaluated against classical baselines (MLP and CNN) using accuracy, precision, recall, F1-score, convergence speed and trainable parameters. Findings Quantum-enhanced models achieved competitive or superior performance compared to classical models with significantly fewer trainable parameters and faster convergence. The Quantum Kernel SVM demonstrated the highest precision (89.1%) and F1-score (88.5%) using only 8 parameters, while VQC models converged up to 2× faster than classical baselines. These results highlight the potential of QML to improve efficiency, robustness and scalability in diagnostic imaging workflows, particularly in resource-constrained or data-limited settings. Research limitations/implications The study relies on simulated quantum environments, which do not capture hardware noise or qubit limitations. Future work will extend to real quantum devices, multimodal data integration and interpretability strategies. Practical implications Quantum kernel methods and lightweight VQC architectures enable faster, resource-efficient diagnostic modeling, making them suitable for clinical environments with limited computational capacity or time-sensitive decision-making needs. Social implications Improving the efficiency and accessibility of medical image analysis has the potential to support faster diagnosis and enhance healthcare delivery, particularly in underserved or resource-limited regions. Originality/value This study provides a practical evaluation of hybrid quantum-classical models applied to multiple real medical imaging datasets in a reproducible simulated environment. It offers an early-stage framework demonstrating how QML techniques can be integrated into existing machine learning pipelines for medical image analysis.