About this Event
Quantum Machine Learning (QML) and Quantum Computing (QC) offer the potential
for improving financial technologies, particularly in fraud detection and market
prediction. This research explores several QML models, including Quantum Support
Vector Classifier (QSVCs), Quantum Graph Neural Networks (QGNNs), and Quantum
Attention Deep Q-Networks (QADQNs), which are designed to process and analyze
complex, graph-structured financial data. The study demonstrates that these models
improve the accuracy of fraud detection and enhance decision-making capabilities in
trading strategies by employing the computational efficiency of QC. The findings highlight
the potential for future development of scalable, efficient, and accurate frameworks based
on QML and QC to address ongoing financial challenges and improve analytical
techniques within the financial sector. These advancements may enable more robust and
data-driven decision-making processes in finance as quantum technologies continue to
evolve.
Speaker:
Dr. Nouhaila Innan is a Research Team Lead at eBRAIN Lab and a Postdoctoral Associate at the Center for Quantum and Topological Systems (CQTS) at New York University (NYU) Abu Dhabi. She earned her PhD in Quantum Machine Learning from Hassan II University of Casablanca, where she also completed her Bachelor's in Physics & Applications and Master's in Physics & New Technologies, specializing in materials and nanomaterials. Her research focuses on quantum machine learning, quantum algorithms, and their applications in fields like finance and cybersecurity. Innan is passionate about mentoring and making quantum technologies accessible through global initiatives.
Moderator:
Dr. Sebastian Zajac, member of QPoland
Event Venue
Online
USD 0.00
