The neurosciences have pioneered the use of quantum technologies for sensing and imaging the brain.Next-generation technologies promise routes towards low-cost, wearable imaging devices with high spatial and temporal resolution.Much of the progress in our understanding of the brain and our ability to assess brain health relies heavily on magnetic resonance imaging (MRI) and quantum technologies that rely on quantum properties of matter or radiation such, e.g., nuclear spin, entanglement or detection of single quanta.These technologies are undergoing a renaissance in funding and research, which has led to some very exciting developments in recent years, for example, reconstructing images, videos [1,2], and the semantics of entire sentences [3] from brain activity and fMRI data alone.The goal of this perspective is to provide a brief overview of some quantum technologies that may have an impact, for example, on the wearability, precision, and cost of these brain reading approaches.Magnetic resonance imaging (MRI) is a noninvasive approach that relies on a quantum effect whereby charged particles can have a spin and thus interact with an external magnetic field that provides information about the conformation of the spins and of the matter that we are probing.One form of MRI known as functional MRI (fMRI)-which measures brain activity by detecting small changes of blood flow in the brain-can now achieve sub-millimetre resolution, albeit typically with a 5 to 10 second time lag that results from the fact that it measures a blood oxygenation level dependent (BOLD) signal.Another noninvasive technology known as magnetoencelphagraphy (MEG) instead measures the magnetic fields generated as a result of the time-varying voltage signals at the cellular level.The quantum technology utilised here lies not in the physics of the signal itself but rather in the detection, which is typically achieved using the extreme sensitivity of superconducting quantum interference devices (SQUIDs).MEG has lower spatial resolution compared to fMRI of order 3 mm but without the BOLD time-lag.It is clear that using fMRI and MEG to image the brain at ever-finer detail is enabling a deeper understanding of how the brain operates.However, fMRI has relatively high costs that severely limit accessibility and the range of experiments is constrained by the fact that lying down in an MRI scanner is not a "natural" environment in which animals or humans operate.MEG based on SQUIDs is moderately more wearable but still expensive, mostly as a result of the experiments needing to be housed in special rooms that shield the instruments from the
The neurosciences have pioneered the use of quantum technologies for sensing and imaging the brain.Next-generation technologies promise routes towards low-cost, wearable imaging devices with high spatial and temporal resolution.Much of the progress in our understanding of the brain and our ability to assess brain health relies heavily on magnetic resonance imaging (MRI) and quantum technologies that rely on quantum properties of matter or radiation such, e.g., nuclear spin, entanglement or detection of single quanta.These technologies are undergoing a renaissance in funding and research, which has led to some very exciting developments in recent years, for example, reconstructing images, videos [1,2], and the semantics of entire sentences [3] from brain activity and fMRI data alone.The goal of this perspective is to provide a brief overview of some quantum technologies that may have an impact, for example, on the wearability, precision, and cost of these brain reading approaches.Magnetic resonance imaging (MRI) is a noninvasive approach that relies on a quantum effect whereby charged particles can have a spin and thus interact with an external magnetic field that provides information about the conformation of the spins and of the matter that we are probing.One form of MRI known as functional MRI (fMRI)-which measures brain activity by detecting small changes of blood flow in the brain-can now achieve sub-millimetre resolution, albeit typically with a 5 to 10 second time lag that results from the fact that it measures a blood oxygenation level dependent (BOLD) signal.Another noninvasive technology known as magnetoencelphagraphy (MEG) instead measures the magnetic fields generated as a result of the time-varying voltage signals at the cellular level.The quantum technology utilised here lies not in the physics of the signal itself but rather in the detection, which is typically achieved using the extreme sensitivity of superconducting quantum interference devices (SQUIDs).MEG has lower spatial resolution compared to fMRI of order 3 mm but without the BOLD time-lag.It is clear that using fMRI and MEG to image the brain at ever-finer detail is enabling a deeper understanding of how the brain operates.However, fMRI has relatively high costs that severely limit accessibility and the range of experiments is constrained by the fact that lying down in an MRI scanner is not a "natural" environment in which animals or humans operate.MEG based on SQUIDs is moderately more wearable but still expensive, mostly as a result of the experiments needing to be housed in special rooms that shield the instruments from the
The development of quantum-enhanced MRI technologies presents a novel frontier for early disease detection and continuous monitoring of human health. The magnetic resonance imaging (MRI) modality has become an essential pillar of modern healthcare systems and is the most widely used imaging technology for human health screening. It provides non-invasive and high-resolution visualisation of both anatomy and dynamics associated with diseases. Nevertheless, there are several diseases for which a MRI alone is not sufficient or highly challenging for early detection. Hence, advanced solutions are required. Quantum-enhanced MRI offers a promising path to overcome the limitations of conventional MRI by providing unprecedented enhancements to sensitivity, contrast, and spatiotemporal resolution compared with the state-of-the-art MRI. These enhancements have the potential to enable the imaging of emerging biomarkers related to several diseases such as Alzheimer’s, Parkinson’s, cardiovascular diseases, metabolic disorders and cancer at the early stage. Also, quantum-enhanced MRI provides non-intrusive monitoring frameworks that allow continuous observation of physiological parameters such as temperature, heart rate, and molecular dynamics while preserving the privacy of individuals. Such frameworks make it possible for a portable/bedside MRI system operating at ambient temperature to remotely monitor human health and wellness anywhere. Quantum-enhanced MRI is, however, still in its infancy and comes with several challenges. It is necessary to conduct a systematic literature review of quantum-enhanced MRI technologies covering its theoretical foundations, technological advances, potential clinical applications, implications for human health monitoring, existing challenges and future directions.
Neuronal electrical activity underlies human cognition including perception, attention, memory, language, and decision-making. Yet its direct, noninvasive measurement in the living human brain remains a fundamental challenge. Existing neuroimaging techniques, including electroencephalography (EEG), magnetoencephalography (MEG), and functional magnetic resonance imaging (fMRI), are limited by trade-offs in sensitivity and spatial or temporal resolution. Here we propose quantum sensing MRI (qsMRI), a noninvasive approach that enables direct detection of neuronal firing-induced magnetic fields using a clinical MRI system. qsMRI exploits endogenous proton (1H) nuclear spins in water molecules as intrinsic quantum sensors and decodes time-resolved phase information from the free induction decay signals to infer neuronal magnetic fields. We validate qsMRI through simulations, phantom experiments, and human studies at rest and during motor tasks, and provide open experimental procedures to facilitate independent rigorous validation. We further present a case study demonstrating potential applications to neurological disorders. qsMRI represents, to our knowledge, the first-in-human application of quantum sensing on a clinical MRI platform and may lay the foundation for a non-BOLD functional imaging modality capable of probing neuronal firing dynamics in both cortical and deep brain regions.
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.
Quantum technology, such as the quantum computers, has attracted significant attention in recent years. In nuclear medicine, powerful and highly sensitive molecular imaging modalities such as PET (Positron Emission Tomography), SPECT (Single Photon Emission CT) and MRI (Magnetic Resonance Imaging) provide accurate morphological and functional information. Exploiting certain aspects of quantum mechanics may bring further improvements in sensitivity, spatial resolution and enable novel capabilities in the field of biomedical imaging and sensing. In this workshop, the possibilities of biomedical applications inspired by quantum technology were discussed. The following exciting topics were covered: quantum entanglement in PET, dynamic nuclear polarization toward more sensitive MRI, quantum sensors based on the229mTh nuclear clock, plans of an advanced high-energy gamma-ray source at CERN, laser-assisted radiation detection, solid-state quantum sensors such as those based on nitrogen vacancy centers in diamond, quantum sensing using cascade multi photons, and a laser-based radioactive isotope analysis.
This research explores how quantum physics is redefining the landscape of medical diagnostics through sensing, imaging, and computation. It examines the principles of quantum mechanics superposition, entanglement, and coherence and their application in developing technologies capable of detecting disease at molecular and cellular levels. Quantum imaging surpasses classical resolution limits, while quantum machine learning enables faster and more accurate analysis of medical data. The study also evaluates real-world clinical applications, including wearable magnetoencephalography, quantum-enhanced MRI, and biosensing for cancer biomarkers. Alongside these advancements, it addresses ethical, economic, and regulatory challenges such as data security, cost, and accessibility. The findings suggest that quantum technologies hold transformative potential to shift medicine from reactive treatment to predictive, preventive care provided innovation remains grounded in transparency, inclusivity, and interdisciplinary collaboration.
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