Project Description

Our research objectives are developing a quantum hardware-efficient hybrid quantum-classical AI algorithms and improving the speed of training and the accuracy of intrusion-detection systems by applying our approach. We aim to come up with efficient quantum AI algorithms with provable guarantees and applications to cybersecurity for autonomous transportation systems.

Start Date

February 2025

Post Doc Qualifications

The researcher must have experience in quantum AI, with a preferred PhD in CS, EE, Statistics, or related areas.

Co-Advisors

Vaneet Aggarwal, vaneet@purdue.edu, IE/ECE https://engineering.purdue.edu/CLANLabs
Satish Ukkusuri, sukkusur@purdue.edu, CE, https://umnilab.github.io/

Bibliography

https://arxiv.org/pdf/2310.11684
https://arxiv.org/pdf/2310.01515
https://www.sciencedirect.com/science/article/abs/pii/S1568494623001175
https://faculty.wharton.upenn.edu/wp-content/uploads/2015/12/COLT-Belloni15_1.pdf