Non-invasive cancer diagnostics aim to detect tumors as early as possible without harming the patient. In this project, I explore how quantum photonics can improve medical imaging by using the quantum properties of light to reach subcellular precision. The main idea is to compare classical optical coherence tomography (OCT) with quantum-enhanced imaging that uses single photons, entangled photon pairs, and squeezed-light states. Using a conceptual device model, I examine how quantum light sources could reduce photonic noise, improve contrast, and allow repeated imaging with minimal damage to sensitive tissues. Literature on quantum imaging, biophotonics, and cancer diagnostics is reviewed to connect these ideas to current research in early tumor detection. These studies report gains in axial resolution, depth penetration, and signal-to-noise ratio when quantum effects are used. The project also considers ethical questions, such as fair access, cost, and data privacy, when introducing advanced quantum technologies into healthcare. Overall, the work suggests that quantum photonics can help build safer, faster, and more accurate imaging systems for cancer diagnostics, while supporting responsible and inclusive medical innovation.
Non-invasive cancer diagnostics aim to detect tumors as early as possible without harming the patient. In this project, I explore how quantum photonics can improve medical imaging by using the quantum properties of light to reach subcellular precision. The main idea is to compare classical optical coherence tomography (OCT) with quantum-enhanced imaging that uses single photons, entangled photon pairs, and squeezed-light states. Using a conceptual device model, I examine how quantum light sources could reduce photonic noise, improve contrast, and allow repeated imaging with minimal damage to sensitive tissues. Literature on quantum imaging, biophotonics, and cancer diagnostics is reviewed to connect these ideas to current research in early tumor detection. These studies report gains in axial resolution, depth penetration, and signal-to-noise ratio when quantum effects are used. The project also considers ethical questions, such as fair access, cost, and data privacy, when introducing advanced quantum technologies into healthcare. Overall, the work suggests that quantum photonics can help build safer, faster, and more accurate imaging systems for cancer diagnostics, while supporting responsible and inclusive medical innovation.
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.
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.
Cancer remains a major global health challenge, necessitating the development of accurate, scalable, and clinically reliable diagnostic approaches. Artificial intelligence (AI) has significantly enhanced cancer detection through advanced analysis of medical imaging, histopathology, and multi-omics data. However, conventional AI models face limitations related to computational scalability, optimization complexity, and data heterogeneity. Quantum computing introduces a fundamentally different computational paradigm based on superposition, entanglement, and probabilistic state evolution. The integration of quantum computing with machine learning referred to as Quantum Artificial Intelligence (QAI) has emerged as a promising approach to address these challenges. This review critically evaluates the role of QAI in cancer detection through a systematic analysis of literature published between 2015 and 2026. The findings suggest that QAI can enhance feature representation, accelerate optimization processes, and improve predictive performance. Hybrid quantum-classical models show potential in tumor classification, multi-omics integration, and real-time clinical decision-making. Despite these advances, significant challenges remain, including hardware limitations, lack of large-scale validation, and ethical concerns. Overall, QAI represents a promising yet emerging paradigm with potential to transform early cancer detection and precision oncology. This review uniquely synthesizes quantum-AI approaches in oncology, emphasizing hybrid quantum-classical models as the most practical translational pathway for clinical adoption.
Quantum imaging is emerging as a transformative approach for biomedical applications, applying nonclassical properties of light, such as entanglement, squeezing, and quantum correlations, to overcome fundamental limits of conventional techniques. These methods promise superior spatial resolution, enhanced signal-to-noise ratios, improved phase sensitivity, and reduced radiation dose, for potentially safer and more precise imaging for delicate biological samples. Here, we present an overview of quantum optical biomedical imaging technologies as well as quantum-inspired imaging methods, including quantum optical coherence tomography, quantum optical microscopy, ghost imaging, multi-parameter quantum imaging, and imaging with quantum-grade cameras. We describe the operating principles, biomedical applications, and unique advantages of each approach, along with the specific challenges for their translation into real-life practice. This review aims to guide future research toward advancing quantum imaging from experimental demonstrations to impactful biomedical tools.
Quantum-enhanced metrology techniques offer ultra-precise timekeeping, allowing for breakthroughs in navigation systems, global positioning, medical and Biological imaging. Quantum metrology, a core application of quantum technologies, leverages non-classical states of light (such as entanglement and squeezing) to enhance measurement precision, sensitivity, and resolution beyond the classical limits, particularly the shot-noise limit. Its application to bio imaging and microscopy offers significant advantages for studying delicate biological specimens, which are often susceptible to damage from the intense light typically required by conventional high-resolution methods. The abstract highlights the potential of quantum properties of light to revolutionize bio imaging by providing a non-invasive, highly sensitive, and high-resolution platform for observing biological processes at fundamental scales.
Medical imaging, a cornerstone of modern diagnostics that underpins early disease detection and personalized therapy, continues to confront fundamental physical limitations inherent to conventional modalities, including suboptimal sensitivity and specificity, limited resolution, and ionizing radiation risks. Quantum imaging, leveraging principles such as entanglement, superposition, and quantum sensing, has emerged as a transformative paradigm with the potential to transcend these limitations. This review systematically summarizes the physical principles, technological advancements, and major clinical applications of quantum technologies in medical imaging. We elaborate on several cutting-edge modalities, including quantum dot probes for targeted imaging and surgical navigation, wearable magnetoencephalography facilitated by optically pumped magnetometers, quantum-enhanced magnetic resonance imaging with nanoscale resolution, quantum optical coherence tomography with dispersion cancellation, photon-counting computed tomography for superior spectral imaging, and nuclear medicine imaging that can visualize the molecular microenvironment. Additionally, the roles of quantum computing and quantum artificial intelligence in accelerating and optimizing the image analysis workflow are also discussed. Furthermore, we analyze the technical bottlenecks and translational barriers that currently hinder the widespread clinical implementation of these emerging technologies. Finally, we outline promising future research directions to accelerate clinical translation, underscoring the transformative potential of quantum medical imaging to advance precision diagnostics and personalized medicine.