Grant: Alliance Grants
University of Calgary
$300,000.00
Optimizing Quantum imaging and sensing with machine learning
EF 2023-24
Résumé
Quantum scanners, both for sensing and imaging, have the potential to overcome the limitations of classical imaging techniques. They allow for high-resolution and high-contrast imaging of samples, even when using an extremely low-level probe signal and in the presence of noise, dispersion, photon loss, and scattering. However, despite their advantages, the use of basic quantum-sensing configurations in real-world applications is hindered by technical limitations in entangled photon sources and detection modules, as well as a lack of advanced data processing techniques. Furthermore, quantum optical imaging techniques typically have a long data acquisition time due to their operation in the single-photon counting regime, and efficient processing of detected signal photons for image reconstruction can be challenging due to noise and intrinsic artifacts that can lead to a loss of structural information about probed objects. Our goal is to develop quantum scanning methods for detecting biological samples and to use the latest state-of-the-art deep learning models to reconstruct images with super-fast and accurate results. Over the five-year duration of this project, we will conduct detailed experimental and theoretical investigations to achieve this goal. Specifically, we aim to develop and validate a quantum scanner based on two methods: quantum optical coherence tomography for skin scanning and quantum illumination for tracking cells/bacteria. In addition, we will utilize advanced classical machine learning methods to further enhance the accuracy and speed of our image reconstructions.This project aligns with Quantum Canada and the National Quantum Strategy, including the Quantum Sensors Challenge program. Our goal is to use advanced machine learning to enhance Canada's leadership in quantum sensing, creating economic benefits and impacting the emerging quantum technology industry.In addition, our project will train future quantum technology workers and develop significantly improved optical quantum sensors/scanners.
Programme
Alliance Grants
Bénéficiaire
Emplacement
Alberta, Canada
Type de bénéficiaire
Academia
Type
Grant
Source
NSERC's Awards Data
Tel que publié
Application id
585961-2023
Name nom
Barzanjeh, Shabir S
Department département
Physics and Astronomy
Organization id
11
Institution établissement
University of Calgary
Province en
Alberta
Province fr
Alberta
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2023
Competition year année de concours
2023
Award amount
300000
Program id
ALLRP
Program name en
Alliance Grants
Program name fr
Subventions Alliance
Group en
RESEARCH PARTNERSHIPS
Group fr
PARTENARIATS DE RECHERCHE
Committee code
96
Committee name en
RPP Internal Decision Cttee
Committee name fr
Cté de décision interne - PPR
Area of application code
1202
Area of application group en
Advancement of knowledge
Area of application group fr
Avancement des connaissances
Area of application en
Physical sciences
Area of application fr
Sciences physiques
Research subject code
3112
Research subject group en
Physics
Research subject group fr
Photonics
Research subject en
Physique
Research subject fr
Photonique
Application title
Optimizing Quantum imaging and sensing with machine learning
Keyword
Biomedical; Deep Learning; Machine Learning; Quantum Imaging; Quantum Optical Coherence Tomography; Quantum Sensing; Quantum Technology
Application summary
Quantum scanners, both for sensing and imaging, have the potential to overcome the limitations of classical imaging techniques. They allow for high-resolution and high-contrast imaging of samples, even when using an extremely low-level probe signal and in the presence of noise, dispersion, photon loss, and scattering. However, despite their advantages, the use of basic quantum-sensing configurations in real-world applications is hindered by technical limitations in entangled photon sources and detection modules, as well as a lack of advanced data processing techniques. Furthermore, quantum optical imaging techniques typically have a long data acquisition time due to their operation in the single-photon counting regime, and efficient processing of detected signal photons for image reconstruction can be challenging due to noise and intrinsic artifacts that can lead to a loss of structural information about probed objects. Our goal is to develop quantum scanning methods for detecting biological samples and to use the latest state-of-the-art deep learning models to reconstruct images with super-fast and accurate results. Over the five-year duration of this project, we will conduct detailed experimental and theoretical investigations to achieve this goal. Specifically, we aim to develop and validate a quantum scanner based on two methods: quantum optical coherence tomography for skin scanning and quantum illumination for tracking cells/bacteria. In addition, we will utilize advanced classical machine learning methods to further enhance the accuracy and speed of our image reconstructions.This project aligns with Quantum Canada and the National Quantum Strategy, including the Quantum Sensors Challenge program. Our goal is to use advanced machine learning to enhance Canada's leadership in quantum sensing, creating economic benefits and impacting the emerging quantum technology industry.In addition, our project will train future quantum technology workers and develop significantly improved optical quantum sensors/scanners.
