Abstract The unique properties of single quantum photons have more potential for many applications in quantum metrology. Here, by using state-of-the-art techniques, the precision of the attenuation measurement of light by propagating in the tissue is enhanced and a method to diagnose cancer in the tissue is proposed. The measurement of light with precision beyond the shot-noise limit and near the ultimate quantum limit can be attained using the heralded single photons (HSPs) generated by the spontaneous parametric down-conversion process. The interaction of HSP with normal and malignant breast tissues announces the presence of its partner. Theoretically and experimentally, a quantum approach to diagnosing cancer by comparing the measured attenuation coefficient of the normal and malignant tissues is demonstrated using the HSPs. We experimentally show that it is possible to improve the attenuation uncertainty due to the scattering of HSPs from the bio-sample to distinguish the malignant tissue from the normal tissue. Here, we use a narrow-band filter to remove the Raman scattering effect and consequently increase the attenuation uncertainty. This method is one of the applications of quantum metrology for precision enhancement which can be used in the histology of unstained tissues and tumor margin detection.
Abstract The unique properties of single quantum photons have more potential for many applications in quantum metrology. Here, by using state-of-the-art techniques, the precision of the attenuation measurement of light by propagating in the tissue is enhanced and a method to diagnose cancer in the tissue is proposed. The measurement of light with precision beyond the shot-noise limit and near the ultimate quantum limit can be attained using the heralded single photons (HSPs) generated by the spontaneous parametric down-conversion process. The interaction of HSP with normal and malignant breast tissues announces the presence of its partner. Theoretically and experimentally, a quantum approach to diagnosing cancer by comparing the measured attenuation coefficient of the normal and malignant tissues is demonstrated using the HSPs. We experimentally show that it is possible to improve the attenuation uncertainty due to the scattering of HSPs from the bio-sample to distinguish the malignant tissue from the normal tissue. Here, we use a narrow-band filter to remove the Raman scattering effect and consequently increase the attenuation uncertainty. This method is one of the applications of quantum metrology for precision enhancement which can be used in the histology of unstained tissues and tumor margin detection.
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.
Optical quantum metrology uses specially-prepared states of light to probe sensitive samples. Choosing a suitable probe state allows one to extract more information per probe photon about an unknown parameter than is possible using any classical technique. In effect, these techniques improve the sensitivity of the measurement whenever the number of probe photons is constrained in some way. This thesis presents experimental and theoretical results on the use of pairs of time-frequency entangled photons in precision measurements. It begins with an overview of necessary background knowledge about quantum optics and quantum information. This is followed by a theoretical discussion on the estimation of a single delay using two-photon interference. We show that constraints on the probe photon's bandwidth complicate the measurement problem, requiring suitable optimization of the two-photon quantum state to avoid losing precision. We then consider the use of two-photon interference for linear spectroscopic measurements. Linear spectroscopy relates the absorption and phase spectrum of a sample to unknown electronic parameters. We describe a proof-of-principle experiment demonstrating that two-photon interferometric spectroscopy can be used for simultaneously measurement of both absorption and phase. This is followed by an information-theoretic discussion of the precision of this technique. For low losses, we show that the method provides phase sensitivity similar to classical interferometry while approaching the optimal absorption precision of any possible measurement. If the absorption and phase values are both related to a single unknown parameter, then our technique provides improved sensitivity to this parameter compared to both classical measurements and other quantum techniques in the presence of realistic levels of loss. We conclude with a discussion of quantum-optical coherence tomography, which is a two-photon interferometric method for determining the material structure of a sample. We present an ongoing experiment which aims to apply the method to tissue-like samples, where the quantum measurement is limited by a poor signal-to-noise ratio. Preliminary results demonstrate that this noise can be largely reduced by suitable frequency-domain filtering. This allows us to precisely determine the position of a scattering center using a few hundred detected photon pairs despite the presence of large amounts of noise. We follow this with a theoretical discussion of signal artifacts which are known to complicate the measured interferogram. We show these artifacts are due to a distinct interferometric effect, which can be distinguished from the signal of interest using the relative time delay between detected photons. We conclude with a brief discussion on possible techniques for artifact removal.
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.
The generation, manipulation and quantification of non-classical light, such as quantum-entangled photon pairs, differs significantly from methods with classical light. Thus, quantum measures could be harnessed to give new information about the interaction of light with matter. In this study we investigate if quantum entanglement can be used to diagnose disease. In particular, we test whether brain tissue from subjects suffering from Alzheimer's disease can be distinguished from healthy tissue. We find that this is indeed the case. Polarization-entangled photons traveling through brain tissue lose their entanglement via a decohering scattering interaction that gradually renders the light in a maximally mixed state. We found that in thin tissue samples (between 120 and 600 micrometers) photons decohere to a distinguishable lesser degree in samples with Alzheimer's disease than in healthy-control ones. Thus, it seems feasible that quantum measures of entangled photons could be used as a means to identify brain samples with the neurodegenerative disease.
We examine a novel diagnostic method suitable for optical biopsy, i.e., the noninvasive in vivo detection of malignant lesions in human tissue. Entangled-photon imaging is an emerging technology based upon the use of non-classical sources of light such as optical parametric oscillators (OPO). These sources generate above-threshold signal and idler beams that have intensity fluctuations highly correlated in space and time (twin beams). It has been shown that low-intensity OPO's make possible high sensitivity absorption measurements of weak targets, below the shot- noise limit. The direct use of this technology for optical biopsy is severely restricted by the large amount of scattering noise associated with light-tissue interaction. We report what is, to our knowledge, the first feasibility study on a differential wavelength, OPO-based setup targeted for mammography. Constraints related to the entanglement time, OPO selection and background suppression are analyzed. The paper concludes with a review of future developments and challenges.
Breast cancer is one of the most prevalent and life-threatening diseases affecting women worldwide, and early detection plays a crucial role in improving survival rates and treatment outcomes.Conventional diagnostic methods such as mammography, ultrasound imaging, and histopathological analysis rely heavily on expert interpretation and classical machine learning techniques.Although classical deep learning models, especially Convolutional Neural Networks (CNNs), have demonstrated impressive performance in medical image analysis, they face inherent limitations related to computational complexity, scalability, and the curse of dimensionality when handling large, high-resolution datasets.With the rapid advancement of quantum computing, Quantum Machine Learning (QML) has emerged as a promising paradigm capable of addressing these challenges by leveraging quantum phenomena such as superposition and entanglement.This research explores the application of Quantum Convolutional Neural Networks (Quantum CNNs or QCNNs) for breast cancer detection, aiming to enhance classification accuracy while reducing computational overhead.The proposed approach integrates quantum circuits with classical preprocessing techniques to extract discriminative features from breast cancer datasets, such as mammographic or histopathological images.By encoding image features into quantum states and performing convolution operations through parameterized quantum circuits, the Quantum CNN model is designed to learn complex patterns more efficiently than classical counterparts.The study evaluates the effectiveness of Quantum CNNs in distinguishing between benign and malignant breast tumors and compares its performance with traditional CNN models.The findings highlight the potential of Quantum CNNs to revolutionize medical image analysis by offering faster convergence, improved generalization, and robustness against noise.This research contributes to the growing field of quantumassisted healthcare systems and demonstrates how quantum computing can be harnessed to address critical challenges in breast cancer diagnosis.