Quantum computing has emerged as a transformative paradigm, capable of tackling complex computational problems that are infeasible for classical methods within a practical timeframe. At the core of this advancement lies the concept of quantum supremacy, which signifies the ability of quantum processors to surpass classical systems in specific tasks. In the context of tomographic image reconstruction, quantum optimization algorithms enable faster processing and clearer imaging than conventional methods. This study further substantiates quantum supremacy by reducing the required projection angles for tomographic reconstruction while enhancing robustness against image artifacts. Notably, our experiments demonstrated that the proposed algorithm accurately reconstructed tomographic images without artifacts, even when up to 50% error was introduced into the sinogram to induce ring artifacts. Furthermore, it achieved precise reconstructions using only 50% of the projection angles from the original sinogram spanning 0° to 180°. These findings highlight the potential of quantum algorithms to revolutionize tomographic imaging by enabling efficient and accurate reconstructions under challenging conditions, paving the way for broader applications in medical imaging, material science, and advanced tomography systems as quantum computing technologies continue to advance.
Quantum computing has emerged as a transformative paradigm, capable of tackling complex computational problems that are infeasible for classical methods within a practical timeframe. At the core of this advancement lies the concept of quantum supremacy, which signifies the ability of quantum processors to surpass classical systems in specific tasks. In the context of tomographic image reconstruction, quantum optimization algorithms enable faster processing and clearer imaging than conventional methods. This study further substantiates quantum supremacy by reducing the required projection angles for tomographic reconstruction while enhancing robustness against image artifacts. Notably, our experiments demonstrated that the proposed algorithm accurately reconstructed tomographic images without artifacts, even when up to 50% error was introduced into the sinogram to induce ring artifacts. Furthermore, it achieved precise reconstructions using only 50% of the projection angles from the original sinogram spanning 0° to 180°. These findings highlight the potential of quantum algorithms to revolutionize tomographic imaging by enabling efficient and accurate reconstructions under challenging conditions, paving the way for broader applications in medical imaging, material science, and advanced tomography systems as quantum computing technologies continue to advance.
Computed tomography (CT) is a non-destructive technique for observing internal images and has proven highly valuable in medical diagnostics. Recent advances in quantum computing have begun to influence tomographic reconstruction techniques. The quantum tomographic reconstruction algorithm is less affected by artifacts or noise than classical algorithms by using the square function of the difference between pixels obtained by projecting CT images in quantum superposition states and pixels obtained from experimental data. In particular, by using quantum linear systems, a fast quadratic unconstrained binary optimization (QUBO) model formulation for quantum tomographic reconstruction is possible. In this paper, we formulate the QUBO model for quantum compressed sensing tomographic reconstruction, which is a linear combination of the QUBO model for quantum tomographic reconstruction and the QUBO model for total variation in quantum superposition-state CT images. In our experiments, we used sinograms obtained by using the Radon transform of Shepp-Logan images and body CT images. We evaluate the performance of the new algorithm by reconstructing CT images using a hybrid solver with the QUBO model computed from each sinogram. The new algorithm was able to obtain a solution within 5 projection images for 30 × 30 image samples and within 6 projection images for 60 × 60 image samples, reconstructing error-free CT images. We anticipate that quantum compressed sensing tomographic reconstruction algorithms could significantly reduce the total radiation dose when quantum computing performance advances.
Abstract Quantum computing is set to revolutionize medical imaging and diagnostics by enhancing speed, accuracy, and the ability to process complex datasets, leading to improved patient outcomes. This review paper provides an extensive comparative study of how quantum-mechanical principles like superposition, entanglement, and interference can overcome the inherent limitations of classical binary computing in the medical field. We explore specific applications in MRI and CT scan reconstruction, the mathematical advantages of the Quantum Fourier Transform (QFT), the integration of Quantum Machine Learning (QML) for automated pathology detection, and the transformative potential of hybrid quantum-classical systems. The paper also addresses current hardware limitations such as qubit decoherence, evaluates materials science innovations in superconducting circuits, and provides a strategic roadmap for the future of quantum-enhanced healthcare. Keywords: Quantum Computing, Medical Imaging, MRI Reconstruction, Qubits, Quantum Machine Learning, Diagnostics, Materials Science Introduction The field of medical imaging has undergone significant transformations over the last few decades, progressing from simple analog X-ray plates to complex digital 3D reconstructions and functional metabolic imaging. However, as the medical community moves toward "Precision Medicine," the demand for ultra-high-resolution data and real-time diagnostic feedback is growing at an exponential rate. Classical computing systems, governed by Moore’s Law, are reaching a physical and algorithmic bottleneck. The sheer volume of data generated by modern 7-Tesla MRI, Dual-Energy CT scans, and high-resolution PET-CT systems is becoming increasingly difficult to process with traditional von Neumann architectures. In modern clinical settings, a single high-resolution volumetric scan can generate several gigabytes of raw data. This data requires intensive signal processing—often taking minutes or even hours—to reconstruct into a format that a radiologist can interpret. In emergency medicine, specifically for stroke or trauma patients, the latency of classical reconstruction algorithms can be the difference between recovery and permanent disability. Quantum computing introduces a fundamental paradigm shift. By leveraging quantum bits (qubits), which utilize the subatomic properties of superposition and entanglement, quantum computers can theoretically perform massive parallel computations that are mathematically impossible for classical binary systems. This paper provides a comprehensive review of these quantum paradigms, comparing their efficiency to classical methods and outlining the path toward clinical implementation. Theoretical Framework: The Physics of Quantum Advantage Classical computers operate on bits, representing a deterministic state of either 0 or 1. In medical imaging, this means algorithms must process pixels or voxels sequentially. Quantum computing utilizes the unique properties of quantum mechanics to alter the fundamental complexity classes of imaging tasks. 2.1 Superposition and Parallelism Unlike a bit, a qubit can exist in a state |⟩=|0⟩+|1⟩, where and are complex probability amplitudes such that ||2+||2=1. This allows n qubits to represent 2n states simultaneously. For a medical image consisting of 10241024 voxels, a quantum system can map the entire state space into a vastly smaller number of physical qubits (approximately 20 qubits for a million voxels). This allows for "Global Optimization," where the computer evaluates all possible image configurations at once to find the one with the least noise. 2.2 Entanglement and Non-Locality Entanglement allows qubits to be correlated in a way that exceeds classical physics. In image processing, this property is being researched to create "Quantum Sensors." These sensors use entangled photons or atoms to detect minute magnetic field variations in MRI far exceeding the sensitivity of classical induction coils. This could lead to MRI machines that do not require massive, expensive superconducting magnets, making them portable and more accessible. 2.3 Quantum Interference and Phase Estimation Quantum interference is used to bias the probability of measuring the correct answer in an algorithm. In image denoising and deconvolution, interference patterns can be engineered to amplify the signal (the anatomical structure) while canceling out the noise (electronic artifacts or motion blur). This process, known as Quantum Phase Estimation (QPE), is a core component of many imaging algorithms that provide a "Quantum Speedup." Applications in Medical Modalities 3.1 Accelerated MRI Reconstruction via Quantum Fourier Transform (QFT) MRI relies on the Fast Fourier Transform (FFT) to convert raw signals from the frequency domain (k-space) into the spatial domain. The classical FFT has a complexity of O(NlogN). In contrast, the Quantum Fourier Transform (QFT) operates with a complexity of O(log2N). This exponential speedup has several critical clinical implications: Real-time Guided Surgery: Surgeons currently rely on static images. QFT could allow for "Live MRI," providing updated feedback during neurosurgery as brain tissue shifts. Pediatric and Geriatric Care: Reducing scan times from 45 minutes to 45 seconds would eliminate the need for anesthesia in children who cannot remain still. Low-Field Imaging: Higher computational power can compensate for lower-quality raw signals, allowing for high-quality images from smaller, less expensive MRI units in rural areas. 3.2 CT and PET Optimization: The Radon Transform CT scans utilize the Radon transform to reconstruct 3D volumes from 2D projections. Classical iterative reconstruction (IR) is computationally expensive and often results in "staircase artifacts." Quantum algorithms, such as the HHL (Harrow-Hassidim-Lloyd) algorithm for linear systems of equations, can solve the massive matrices required for CT reconstruction with logarithmic scaling. This allows for higher resolution while simultaneously reducing the radiation dose required for the patient. 3.3 Comparative Analysis: A Detailed Quantitative Review Table 1: Comparative Analysis: Classical vs. Quantum Computing in Medical Imaging Feature Classical Computing (Binary) Quantum Computing (Qubit) Clinical Impact & Consequences Basic Data Unit Bits (0 or 1) Qubits (Superposition) Exponentially higher data density and state representation. Logic Gates Boolean Logic (AND, OR, NOT) Unitary Gates (Hadamard, CNOT) Ability to explore multiple logical paths simultaneously. Processing Power Sequential or Vectorized (CPU/GPU) Massively Parallel (Global) Enables real-time diagnostic feedback during surgical procedures. MRI Scaling O (N log N) Complexity O (log2 N) Complexity Theoretical 100x speedup in image reconstruction times. Algorithmic Aim Gradient Descent (Local Minima) Quantum Annealing (Global Minima) Superior noise reduction and image clarity by finding the "true" global optimum. Storage & Memory Linear Growth Exponential Growth (2n) Efficient management of high-dimensional datasets like whole-genome sequencing. Radiation Safety Higher Dose (to maintain SNR) Lower Dose (Q-enhanced SNR) Significant reduction in long-term radiation exposure for chronic patients. Biomarker Detection Limited by pixel resolution/noise Enhanced via Quantum Interference Early-stage detection of micro-metastases and neurodegenerative markers. Hardware State Mature (Silicon CMOS) Emerging (NISQ Era) Shift toward hybrid architectures in the immediate clinical future. The transition from classical to quantum paradigms represents a shift from linear deterministic processing to exponential probabilistic optimization. While classical systems are robust and reliable for standard data management, quantum systems address the "computational bottleneck" inherent in high-resolution volumetric imaging (7T MRI, 4D-CT). The primary clinical advantage identified is the acceleration of the Radon and Fourier transforms, which reduces patient wait times and allows for lower-dose diagnostic protocols without sacrificing image quality. Integration with Artificial Intelligence (AI) and Machine Learning The synergy between Quantum Science and Artificial Intelligence Quantum Machine Learning (QML) is a primary focus for current research at institutions like eminent University. 4.1 Quantum Neural Networks (QNN) and Feature Spaces In classical AI, deep learning models often struggle with the "curse of dimensionality" when analyzing 4D imaging datasets. QNNs utilize "Quantum Feature Maps" to project data into a high-dimensional Hilbert space where classes of data (e.g., malignant vs. benign) become linearly separable. This leads to significantly higher sensitivity in automated mammography and lung cancer screening. 4.2 Automated Segmentation and Pathology Detection Automated segmentation involves the computer outlining the boundaries of an organ or a tumor. Classical AI requires thousands of labeled images to learn these boundaries. Preliminary studies suggest that QML algorithms can achieve higher accuracy with 70% less training data because the quantum kernels are inherently better at recognizing complex geometric patterns in biological tissue. Materials Science and Hardware Implementation The transition from theory to clinical practice is a challenge of Materials Science. The qubits must be isolated from all external interference to prevent "decoherence." Superconducting Qubits: Currently favored by IBM and Google, these use Josephson junctions made of superconducting materials like Niobium. They require dilution refrigerators to reach temperatures colder than deep space (15 mK). Trapped Ion Qubits: Utilize individual atoms (li
Quantum algorithms have emerged as a transformative approach to address the computational challenges of high-dimensional image processing tasks. This research investigates the integration of quantum computing principles, such as Quantum Fourier Transform (QFT) and Quantum Principal Component Analysis (QPCA), to optimize processes like edge detection, noise reduction, and image segmentation. These quantum-enhanced techniques promise significant improvements in computational efficiency and accuracy compared to classical methods, especially when handling complex visual data in real-time applications like medical imaging, satellite imagery, and autonomous systems. Additionally, the study explores hybrid quantum-classical architectures to bridge current limitations in noisy intermediate-scale quantum (NISQ) devices, ensuring resource optimization and scalability. The potential of quantum algorithms to provide faster, more precise solutions is evaluated alongside challenges like error mitigation and algorithmic stability. This research highlights the future role of quantum computing in revolutionizing image processing, paving the way for breakthroughs in computer vision and artificial intelligence.
Over the past few decades, medical imaging has revolutionized the way clinicians diagnose, monitor, and treat diseases. However, despite significant advancements in imaging resolution, contrast, and computational reconstruction, several challenges remain unresolved — especially in terms of early-stage detection, noise reduction, and functional imaging at the cellular or molecular level. In recent years, quantum physics has emerged as a disruptive force with the potential to drastically enhance medical imaging technologies through the application of its fundamental principles such as quantum superposition, entanglement, tunneling, and spin dynamics. This research explores the intersection of quantum mechanics and medical imaging engineering, with a primary focus on how quantum phenomena are being utilized to elevate the performance and capability of key imaging modalities like Magnetic Resonance Imaging (MRI), Positron Emission Tomography (PET), and optical-based quantum imaging. The study presents an in-depth analysis of how quantum concepts are applied in practice — such as the role of spin alignment and magnetic resonance in MRI, the use of entangled photon pairs in PET scanning, and the integration of quantum dots in high-contrast optical imaging. Beyond existing technologies, the paper investigates cutting-edge developments including quantum-enhanced sensors, quantum computing for real-time image reconstruction, and the promise of quantum artificial intelligence in image interpretation. By analyzing both theoretical frameworks and experimental findings, the study highlights the significant advantages that quantum technologies bring — such as improved spatial resolution, higher signal-to-noise ratio, and reduced patient exposure to radiation. Despite its promise, the integration of quantum physics into medical imaging is not without challenges. The research discusses limitations such as system decoherence, scalability, clinical costs, and the gap between laboratory research and clinical deployment. In conclusion, this paper argues that while still emerging, the application of quantum physics holds transformative potential for the future of diagnostic imaging, and could ultimately lead to more accurate, efficient, and personalized healthcare.
Computed tomography (CT) has been developed as a nondestructive technique for observing minute internal images in samples. It has been difficult to obtain photorealistic (clean or clear) CT images due to various unwanted artifacts generated during the CT scanning process, along with the limitations of back-projection algorithms. Recently, an iterative optimization algorithm has been developed that uses an entire sinogram to reduce errors caused by artifacts. In this paper, we introduce a new quantum algorithm for reconstructing CT images. This algorithm can be used with any type of light source as long as the projection is defined. Assuming an experimental sinogram produced by a Radon transform, to find the CT image of this sinogram, we express the CT image as a combination of qubits. After acquiring the Radon transform of the undetermined CT image, we combine the actual sinogram and the optimized qubits. The global energy optimization value used here can determine the value of qubits through a gate model quantum computer or quantum annealer. In particular, the new algorithm can also be used for cone-beam CT image reconstruction and for medical imaging.
The integration of quantum computing into image processing represents a significant advancement in computational technology, offering transformative capabilities that enhance performance, efficiency, and accuracy. This chapter explores hybrid quantum-classical algorithms, focusing on how quantum subroutines can be integrated into classical image processing workflows. A comparative analysis of various quantum image encoding techniques highlights their strengths and limitations, paving the way for optimized applications across diverse fields. Key advantages of quantum-enhanced image processing, such as improved speed in high-resolution data analysis and superior optimization methods, are discussed in relation to real-world applications, including medical imaging and computer vision. The chapter anticipates future developments in quantum technology, emphasizing the need for interdisciplinary collaboration to unlock the full potential of quantum computing in digital image applications. By addressing the critical gaps in current methodologies, this work aims to inspire further research and innovation in the realm of quantum-enhanced image processing.