Grant: Alliance Grants
Université de Sherbrooke
$292,500.00
Quantum computing meets molecular chemical diversity in the environment: application to PFAS
FY 2024-25
Summary
The proposed multisectoral partnership research project seeks to develop an innovative hybrid classical-quantum machine learning workflow to detect and identify persistent organic pollutants, the per- and poly-fluoroalkyl substances (PFAS), in complex environmental matrices. These forever chemicals are a class of man-made products suspected to cause irreversible human and ecological damage. The project is intended as as a way to use quantum information sciences in connecting for the first time, analytical chemistry, quantum chemistry, and the quantum computer through machine learning. We propose to develop a data processing workflow to identify structurally fluorinated compounds in environmental matrices using ion mobility mass spectrometry and quantum computing. The quantum search approach has the potential to accelerate and increase the precision in the identification of the structural features of PFAS.
Program
Alliance Grants
Recipient
Location
Quebec, Canada
Recipient Type
Academia
Type
Grant
Source
NSERC's Awards Data
As published
Application id
588330-2023
Name nom
Guéguen, Céline C
Department département
Chimie
Organization id
68
Institution établissement
Université de Sherbrooke
Province en
Québec
Province fr
Québec
Country en
CANADA
Country fr
CANADA
Fiscal year exercice financier
2024
Competition year année de concours
2023
Award amount
292500
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
400
Area of application group en
Environment
Area of application group fr
Environnement
Area of application en
Environment
Area of application fr
Environnement
Research subject code
3502
Research subject group en
Analytical chemistry
Research subject group fr
Chimie analytique
Research subject en
Organic analytical chemistry
Research subject fr
Chimie analytique organique
Field of research list names en
Not available
Field of research list names fr
Non disponible
Socioeconomic objective list names en
Not available
Socioeconomic objective list names fr
Non disponible
Application title
Quantum computing meets molecular chemical diversity in the environment: application to PFAS
Keywords
graph neural network; ion mobility spectrometry; neural network; perfluorinated compounds; pfas; quantum kernel; quantum machine learning; reservoir computing; smiles; xenobiotics
Application summary
The proposed multisectoral partnership research project seeks to develop an innovative hybrid classical-quantum machine learning workflow to detect and identify persistent organic pollutants, the per- and poly-fluoroalkyl substances (PFAS), in complex environmental matrices. These forever chemicals are a class of man-made products suspected to cause irreversible human and ecological damage. The project is intended as as a way to use quantum information sciences in connecting for the first time, analytical chemistry, quantum chemistry, and the quantum computer through machine learning. We propose to develop a data processing workflow to identify structurally fluorinated compounds in environmental matrices using ion mobility mass spectrometry and quantum computing. The quantum search approach has the potential to accelerate and increase the precision in the identification of the structural features of PFAS.
