This paper presents a hybrid quantum-classical framework for real-time hospital operations that addresses three routine sources of delay and risk: operating room (OR) rescheduling under disruptions, dynamic ICU/ED staffing under shocks, and MRI throughput with quality control. The approach integrates shallow, latency-bounded quantum optimization within established scheduling pipelines to accelerate diversified search over combinatorial decision spaces under strict deadlines, while classical solvers enforce feasibility, safety, and deterministic fallbacks. In parallel, sensing-guided MRI control provides realtime field/SNR characterization that drives adaptive acquisition and stabilizes AI reconstruction, reducing rescans without compromising diagnostic integrity. The system comprises: (1) a realtime OR rescheduling module that preserves credentialing, safety, and resource constraints; (2) a dynamic ICU/ED assignment module that maintains safe coverage and fairness with acuityand skill-mix awareness; and (3) an MRI module that couples on-scanner quality control with physics-consistent reconstruction to shorten scan times. Evaluation emphasizes wall-clock latency, constraint satisfaction, utilization, and image-quality indices-metrics aligned to patient access, safety, and operational resilience. Results indicate that shallow hybrid quantum methods, when fused with robust classical controllers and sensing feedback, can deliver reliable, incremental improvements under real-time hospital conditions, while providing auditable guardrails and failsafe rollbacks suitable for near-term deployment.
This paper presents a hybrid quantum-classical framework for real-time hospital operations that addresses three routine sources of delay and risk: operating room (OR) rescheduling under disruptions, dynamic ICU/ED staffing under shocks, and MRI throughput with quality control. The approach integrates shallow, latency-bounded quantum optimization within established scheduling pipelines to accelerate diversified search over combinatorial decision spaces under strict deadlines, while classical solvers enforce feasibility, safety, and deterministic fallbacks. In parallel, sensing-guided MRI control provides realtime field/SNR characterization that drives adaptive acquisition and stabilizes AI reconstruction, reducing rescans without compromising diagnostic integrity. The system comprises: (1) a realtime OR rescheduling module that preserves credentialing, safety, and resource constraints; (2) a dynamic ICU/ED assignment module that maintains safe coverage and fairness with acuityand skill-mix awareness; and (3) an MRI module that couples on-scanner quality control with physics-consistent reconstruction to shorten scan times. Evaluation emphasizes wall-clock latency, constraint satisfaction, utilization, and image-quality indices-metrics aligned to patient access, safety, and operational resilience. Results indicate that shallow hybrid quantum methods, when fused with robust classical controllers and sensing feedback, can deliver reliable, incremental improvements under real-time hospital conditions, while providing auditable guardrails and failsafe rollbacks suitable for near-term deployment.
Real-time healthcare workforce scheduling represents a critical optimization challenge with direct implications for patient care quality and operational efficiency. This article introduces a quantum computing approach to dynamic hospital staff rescheduling that addresses the complex constraints of medical workforce allocation when facing unexpected absences. By formulating the problem as a quadratic unconstrained binary optimization (QUBO) model suitable for quantum processing, demonstrate how quantum annealing and gate-based quantum algorithms can rapidly identify optimal staff substitutions while balancing factors such as specialist qualifications, shift duration, fatigue management, and geographical proximity. The simulation results indicate that quantum-accelerated scheduling can significantly reduce the average time to fill critical absences compared to classical heuristic methods while maintaining high adherence to staffing quality metrics. This has substantial potential to mitigate the downstream clinical impacts of staffing disruptions, particularly in high-acuity hospital departments where timely specialist presence is essential for patient outcomes.
Abstract Task offloading frameworks play a crucial role in modern healthcare by optimizing resource utilization, reducing computational burdens, and enabling real-time medical decision-making. However, existing Digital Twin (DT)-based healthcare models suffer from high latency, inefficient resource allocation, cybersecurity vulnerabilities, and computational limitations when processing large-scale patient data. These constraints pose significant risks in time-sensitive applications such as ICU monitoring, robotic-assisted surgeries, and telemedicine. To address these limitations, this paper introduces a Quantum-Enhanced DT-IoT framework, integrating Artificial Intelligence (AI), Quantum Computing (QC), DT, and the Internet of Things (IoT) for real-time, secure, and efficient healthcare task offloading. The proposed system introduces two key optimization algorithms: (1) DTH-ATB-MAPPO, which dynamically adjusts task scheduling and resource distribution, and (2) AQDT-IoT, which enhances computational efficiency and cybersecurity compliance in 6 G-enabled IoT networks. By leveraging Approximate Amplitude Encoding (AAE) and Grover’s search, the framework enhances task offloading efficiency, enabling faster decision-making and optimized resource distribution across 6 G-enabled IoT networks. Empirical evaluations show that quantum preprocessing improved Task Offloading Success Rate (TOSR) by 32% and reduced the Error Rate (ER) by 80%, significantly outperforming traditional DT-based healthcare models. These enhancements enable. Additionally, theoretical analysis demonstrates computational speed enhancements, adaptive cybersecurity mechanisms, and improved system scalability, positioning this framework as a viable candidate for future cloud-based quantum healthcare infrastructures, even in resource-constrained hospital environments.
Hybrid quantum–classical simulation offers a novel computational paradigm for addressing the increasing complexity of modern manufacturing systems. As production lines evolve into highly interconnected, data-rich environments, conventional simulation and optimization techniques often struggle to capture nonlinear interactions, dynamic bottlenecks, and stochastic fluctuations in real time. This study proposes a hybrid framework that integrates quantum-inspired state encoding, variational quantum optimization, and classical discrete-event simulation to model and predict bottleneck behavior with enhanced efficiency. By leveraging quantum subroutines—particularly Variational Quantum Eigensolvers (VQEs) and Quantum Approximate Optimization Algorithms (QAOA)—the framework accelerates combinatorial scheduling searches and dynamic resource allocation tasks. Classical processors, in turn, handle high-fidelity system modeling and domain-specific constraints. Preliminary experimental simulations demonstrate that the hybrid approach can identify emergent bottlenecks earlier than purely classical methods and improve process throughput by up to 18% in benchmark scenarios. These results highlight the transformative potential of hybrid quantum–classical systems for next-generation smart factories, enabling more resilient, adaptive, and energy-efficient manufacturing operations.
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
Future 6G-enabled smart hospital infrastructures will support latency-critical medical operations such as robotic surgery, autonomous monitoring, and real-time clinical decision systems, which require communication mechanisms that ensure both ultra-low latency and long-term cryptographic security. Existing security solutions either rely on classical cryptographic protocols that are vulnerable to quantum attacks or deploy isolated post-quantum primitives without providing a unified framework for secure real-time medical command transmission. This research presents a latency-aware, multi-layered post-quantum security architecture for 6G-enabled smart hospital environments. The proposed framework establishes an end-to-end secure command transmission pipeline that integrates hardware-rooted device authentication, post-quantum key establishment, hybrid payload protection, dynamic access enforcement, and tamper-evident auditing within a coherent system design. In contrast to existing approaches that focus on individual security mechanisms, the architecture introduces a structured integration of Kyber-based key encapsulation and Dilithium digital signatures with hybrid AES-based encryption and legacy-compatible key transport, while Physical Unclonable Function authentication provides hardware-bound device identity verification. Zero Trust access control, metadata-driven anomaly detection, and blockchain-style audit logging provide continuous verification and traceability, while threshold cryptography distributes cryptographic authority to eliminate single points of compromise. The proposed architecture is evaluated using a discrete-event simulation framework representing adversarial conditions in realistic 6G medical communication scenarios, including replay attacks, payload manipulation, and key corruption attempts. Experimental results demonstrate improved security and operational efficiency, achieving a 48% reduction in detection latency, a 68% reduction in false-positive anomaly detection rate, and a 39% improvement in end-to-end round-trip latency compared to conventional RSA-AES-based architectures. These results demonstrate that the proposed framework provides a practical and scalable approach for achieving post-quantum secure and low-latency command transmission in next-generation 6G smart hospital systems.
Parallel imaging techniques, such as GeneRalized Autocalibrating Partially Parallel Acquisitions (GRAPPA) play an important role in Magnetic Resonance Imaging (MRI) by significantly reducing scan times and enhancing patient comfort without compromising image quality. GRAPPA's algorithmic framework involving calibration and synthesis stages is critical in reconstructing high-quality images. However, the computational load of the calibration stage, especially with large convolutional kernel sizes or an increased number of receiver coils, poses a significant bottleneck and limits its efficiency and applicability in clinical settings. In this paper, we introduce an approach by proposing a hybrid software architecture that integrates quantum computing into the GRAPPA reconstruction process. Our method exploits the computational capabilities of quantum computing to accelerate the calibration phase, thereby enabling real-time processing speeds. Through experimentation, we demonstrate that the quantum-enhanced approach can expedite the calibration process to around 10–20 milliseconds of Quantum Processing Unit (QPU) programming time for each Linear Time-Invariant (LTI) system solved. The method maintains the integrity of calibration outcomes, achieving results on par with conventional central processing unit (CPU)-based processes. The result represents the progress towards real-time MRI reconstruction by reducing clinical MRI workflows times, improving patient throughput, and potentially enabling new diagnostic capabilities. Solving LTI system with more attributes on D-Wave quantum computer will be studied to show advantages of QPU to CPU in the future work.