Abstract Technetium-99m (⁹⁹ᵐTc) radiopharmaceuticals account for more than 80% of diagnostic nuclear medicine procedures, yet their design has remained largely empirical, with minimal integration of quantum-mechanical stability. We present a quantum-entropy optimization framework demonstrating a statistically robust inverse correlation (ρ = -0.76 ± 0.05,p < 0.001) between Rényi-2 entropy (S₂) and quantum state purity (Tr[ρ²]) across ⁹⁹ᵐTc decay pathways. To formalize this relationship and for further research, we propose QCuries (Quantum Curies) as a unit for quantifying quantum-augmented activity, defined as 1 QCurie = 1 Curie × (1 - e⁻ᴿᵉ[ρ]), which reduces to the classical Curie under full decoherence. Our hybrid quantum-classical neural networks (QNN–ANN), trained on ab initio Nikiforov–Uvarov solutions and data from Nuclear information repositories, achieves a 32% accuracy gain over classical ANN baselines in stability and information-theoretic parameter predictions. Predicted phenomena include a 660-attosecond coherence threshold for β⁻ decay, high-purity α emissions (98% at 0.25 nat entropy), and >18% deviations from linear dosimetry in high-entropy regimes(S₂ > 1.5 nat). These results reveal a computationally defined ‘quantum Goldilocks zone’(0.5 < S₂ < 1.5 nat; 0.7-1.2 QCuries), which may guide the optimization of diagnostic tracers pending clinical validation. While clinical validation remains ongoing, this framework provides a physics-grounded path toward more predictive radiopharmaceutical design and may guide future regulatory standards.
Abstract Technetium-99m (⁹⁹ᵐTc) radiopharmaceuticals account for more than 80% of diagnostic nuclear medicine procedures, yet their design has remained largely empirical, with minimal integration of quantum-mechanical stability. We present a quantum-entropy optimization framework demonstrating a statistically robust inverse correlation (ρ = -0.76 ± 0.05,p < 0.001) between Rényi-2 entropy (S₂) and quantum state purity (Tr[ρ²]) across ⁹⁹ᵐTc decay pathways. To formalize this relationship and for further research, we propose QCuries (Quantum Curies) as a unit for quantifying quantum-augmented activity, defined as 1 QCurie = 1 Curie × (1 - e⁻ᴿᵉ[ρ]), which reduces to the classical Curie under full decoherence. Our hybrid quantum-classical neural networks (QNN–ANN), trained on ab initio Nikiforov–Uvarov solutions and data from Nuclear information repositories, achieves a 32% accuracy gain over classical ANN baselines in stability and information-theoretic parameter predictions. Predicted phenomena include a 660-attosecond coherence threshold for β⁻ decay, high-purity α emissions (98% at 0.25 nat entropy), and >18% deviations from linear dosimetry in high-entropy regimes(S₂ > 1.5 nat). These results reveal a computationally defined ‘quantum Goldilocks zone’(0.5 < S₂ < 1.5 nat; 0.7-1.2 QCuries), which may guide the optimization of diagnostic tracers pending clinical validation. While clinical validation remains ongoing, this framework provides a physics-grounded path toward more predictive radiopharmaceutical design and may guide future regulatory standards.
The rapid growth of healthcare data demands predictive models capable of handling complexity, noise, and imbalance inherent in clinical datasets. This study explores the integration of quantum computing with artificial intelligence to develop Hybrid Quantum–AI models for healthcare prediction. The proposed framework combines classical preprocessing and feature extraction with quantum neural networks (QNNs) and quantum kernel methods to enhance predictive performance on diverse healthcare tasks, including disease diagnosis and postoperative complication prediction. Empirical evaluation across benchmark and clinical datasets demonstrates that hybrid quantum models achieve superior sensitivity, precision, and calibration compared to traditional machine learning approaches, particularly under conditions of data imperfection and small sample size. Beyond performance, this work investigates practical barriers to clinical adoption, including hardware limitations, scalability, interpretability, and ethical compliance. The results highlight that while current quantum hardware remains a constraint, hybrid approaches already offer tangible benefits in predictive accuracy and robustness. The study concludes by outlining a roadmap for real-world implementation, emphasizing the need for interpretable hybrid architectures, federated data strategies, and regulatory alignment to enable the transition of quantum–AI healthcare solutions from research to clinical practice.
Quantum Neural Networks (QNNs) represent a compelling convergence of quantum mechanics and machine learning, yet their practical deployment remains constrained by noise, limited qubit counts, and the cost of full quantum execution. Hybrid quantum-classical (HQC) training offers a pragmatic path forward: parameterized quantum circuits (PQCs) process information in exponentially large Hilbert spaces while classical optimizers update parameters via gradient methods accessible on today's hardware. We present a modular HQC training framework, formalising the parameter-shift rule for hardware-compatible gradient estimation, characterising the barren-plateau phenomenon through a variance-decay analysis, and introducing noise-aware training strategies for NISQ devices. Empirical results from state-vector and shot-based simulation confirm measurable quantum advantage on quantum-structured tasks, with the phase classification benchmark improving +12.1% over a classical MLP baseline. The accompanying open-source library provides a clean abstraction between quantum simulation and classical optimisation, serving as a foundation for near-term quantum machine learning research.
Background: Quantum Neural Networks (QNNs) combine quantum computing and artificial intelligence to provide powerful solutions for high-dimensional data analysis. In magnetic resonance imaging (MRI), they address the challenges of advanced imaging sequences and data complexity, enabling faster optimization, enhanced feature extraction, and real-time clinical applications. Methods: A literature review using Scopus, PubMed, IEEE Xplore, ACM Digital Library and arXiv identified 84 studies on QNNs in MRI. After filtering for peer-reviewed original research, 20 studies were analyzed. Key parameters such as datasets, architectures, hardware, tasks, and performance metrics were summarized to highlight trends and gaps. Results: The analysis identified datasets supporting tasks like tumor classification, segmentation, and disease prediction. Architectures included hybrid models (e.g., ResNet34 with quantum circuits) and novel approaches (e.g., Quantum Chebyshev Polynomials). Hardware ranged from high-performance GPUs to quantum-specific devices. Performance varied, with accuracy up to 99.5% in some configurations but lower results for complex or limited datasets. Conclusions: The findings provide the first glimpse into the potential of QNNs in MRI, demonstrating accuracy and specificity in diagnostic tasks and biomarker detection. However, challenges such as dataset variability, limited quantum hardware access, and reliance on simulators remain. Future research should focus on scalable quantum hardware, standardized datasets, and optimized architectures to support clinical applications and precision medicine.
The integration of Artificial intelligence (AI) and Quantum computing (QC) is redefining the future of pharmaceutical research and development.While AI-particularly Machine learning (ML) and deep learning-accelerates target identification, virtual screening, and de novo drug design, it often suffers from interpretability issues, data bias, and limited mechanistic understanding.Conversely, QC leverages quantum mechanics to achieve unprecedented accuracy in molecular simulations, yet remains restricted by noise, limited qubits in the Noisy Intermediate-Scale Quantum (NISQ) era, and high costs.Emerging hybrid quantum-classical algorithms and Quantum Machine Learning (QML) approaches bridge these gaps, enabling high-fidelity in silico drug discovery workflows.Despite these advances, the regulatory environment has not kept pace.Current frameworks, such as Physiologically Based Pharmacokinetic (PBPK) modelling and Quantitative Structure-Activity Relationship (QSAR) models, provide limited guidance for validating complex AI-QC systems.Challenges include the lack of explainable artificial intelligence (XAI), uncertainty in verifying quantum outputs, data integrity, and accountability in hybrid pipelines.These gaps highlight the urgent need for regulatory science to adopt dynamic validation frameworks, algorithmic audits, and international harmonization.This review critically examines the technological and regulatory dimensions of AI-QC synergy in drug discovery, outlining key applications, limitations, and ethical considerations.We propose pathways for explainability-by-design, continuous learning oversight, and regulatory sandboxes to facilitate safe and effective adoption.The synergy of AI and QC offers the potential to accelerate timelines, reduce costs, and deliver precision medicines globally-but its promise can only be realized through proactive, transparent, and ethical governance.
Precision medicine aims to tailor healthcare by integrating individual genetic, epigenetic, transcriptomic, proteomic, and clinical data, collectively referred to as multi-omic data. However, the scale and complexity of such multi-omics datasets challenge classical computing approaches. Quantum computing, which leverages superposition and entanglement (quantum-level correlations between particles), offers a fundamentally new paradigm for accelerating molecular simulations, biomarker discovery, and high-dimensional data analysis. This review explores the convergence of quantum computing and it's potential to provide unmet needs in precision biomedicine research, with emphasis on applications in diagnostic modeling, multi-omic data integration and drug discovery. We highlight early proof-of-concept studies demonstrating the use of quantum machine learning for disease prediction, quantum algorithms for protein folding, and quantum generative models for novel drug design. Hybrid quantum-classical workflows are also already enabling gene network inference and prioritization of variants of uncertain significance, the latter of which is a major focus of multi-omic research worldwide. Emerging directions include digital twin simulations and real-time clinical decision support powered by quantum models. Looking ahead, the long-term vision for quantum computing in biomedicine involves in silico modeling of entire biological systems to simulate cellular responses to perturbations like drug treatments, enabling clinicians to test therapies in virtual patients before real-world application. Despite these advances, practical implementation remains limited by hardware constraints, qubit decoherence, algorithm scalability, and regulatory barriers. Nonetheless, as quantum hardware evolves and AI-aligned quantum algorithms mature, their integration holds transformative potential. Quantum computing may eventually shorten diagnostic timelines, improve therapeutic precision, and make biomedical innovation more globally accessible. We outline a roadmap for translating these technologies into next-generation precision medicine.
Renal cell carcinoma (RCC) is a lethal urological cancer, and high mortality rates associated with the disease are frequently due to late diagnosis and the functional diversity of a tumor. Current diagnostic paradigms, mostly depending on radiological visualization and histopathological evaluation, are plagued with problems in the form of inter-observer variation and lag time in detection, and are also inadequate in recapitulating the heterogeneous nature of the tumor microenvironment. To address these challenges, in this work we propose an intelligent diagnostic framework combining quantumenhanced expandable neural networks (Q-ENNs) and medical imaging for precision oncology. In contrast to traditional DL models, Q-ENNs leverage quantum parallelism and entanglement-induced strategies to traverse high-dimensional imaging spaces by outperforming feature extraction and diagnostic performance. The scalable design can flexibly scale network layers and qubits according to clinical requirements, allowing multi-stage RCC diagnosis. Q-ENNs trained on multimodal imaging data (including CT, MRI, and histopathological slides) outperform state-of-theart convolutional neural networks (CNNs) in the tasks of tumor detection, staging, and subtype classification. In addition, the quantum-assisted optimization approach accelerates the convergence and alleviates local minima, which are not only benefit for better discrimination performance but also robust diagnostic results among different populations of patients. We validate our results by clinical evaluation, where a better trade-off between sensitivity and specificity with higher interpretability are obtained with respect to state-of-the-art through explainable quantum feature mapping, as claimed in precision oncology terms. The system we present is intended to be deployed in a realistic clinical setting, supporting computational performance (scalability), integration in the existing radiology workflow, and generic imaging modalities. By interfacing principles of quantum computing with clinical applications, this study paves the road for the future of oncology diagnostics. These discoveries underpin that quantum-driven diagnostic systems could change the way RCC is assessed, providing for earlier and more reliable detection, help in the personalization of treatment and thus enhance patient outcomes in precision oncology.