Project Machine-Learning Inspired Quantum Annealing Program Engineering First Time Researcher (FTR) Fellowship Term Spring 2025 Status Closed Research Area Quantum Computing, Machine Learning, Nanophotonics Description Our group is multidisciplinary, with focuses in emerging generative modelling techniques like adversarial machine learning and quantum-classical models and their applications in nanophotonics. Currently, we are exploring the applications of machine learning and quantum annealing for complex challenges in material sciences, namely material discovery and characterization, parameter optimization and algorithmic benchmarking. Direct applications are as follows: 1. Metasurface Design via Topology Optimization 2. Parameter Fine-Tuning for Single Photon Emitters (SPEs) and Various Two-Level Quantum Systems Note: Priority will be given to students with coursework background in algorithms, preferably quantum algorithms, linear algebra, and/or basic machine learning principles. However, we encourage undergraduates to apply even if they have experience in only one of these areas. Supervisor Alexandra Boltasseva Mentor Yuheng Chen Type of work required We expect the following possible outcomes of our First Time Researchers:
1. Conducting in-depth theoretical analysis and literature reviews on quantum annealing and hybrid quantum-classical learning models
2. Designing and testing code for new algorithms and simulations
3. Benchmarking implementations against existing frameworks
4. Collaborative Problem Solving and Discussions Websites https://engineering.purdue.edu/~aeb/ Qualifications Coursework in software engineering (proficiency in Python, C, or Linux), machine learning, quantum mechanics Credit Hours 0 Weekly Hours 10 (estimated)