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Enhancing QML trainability in noisy quantum systems
This project will develop novel circuit metrics to predict model performance under realistic noise conditions, offering a practical approach to enhancing QML trainability. The research will investigate optimal parameter resilience across different circuit depths, qubit counts, and problem types, while comparing overparameterized and underparameterized regimes. Additionally, circuit metrics will be developed to predict model performance under realistic noise conditions. These efforts aim to enhance the reliability and scalability of variational quantum algorithms.
Faculty Supervisor:
Mohammad Kohandel
Student:
Partner:
Ivan Franko National University of Lviv
Discipline:
Physics
Sector:
Quantum Science; Artificial Intelligence
University:
University of Waterloo
Program:
Globalink Research Award
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