Research Intern, AI and Quantum Algorithms for Optimization

What you'll do

• Design, implement, and analyze quantum, classical, and hybrid quantum-classical algorithms for optimization problems.
• Benchmark quantum optimization approaches on IBM quantum hardware and simulators.
• Develop quantum-centric supercomputing workflows that partition problems across QPUs and classical HPC resources.
• Establish performance baselines against state-of-the-art classical solvers using complexity and resource-estimation analyses.
• Explore the use of AI and agentic systems for algorithm design, hyperparameter search, and automated experiment orchestration.
• Implement research prototypes in Python using Qiskit and the scientific Python ecosystem.
• Present research results in team meetings and contribute to technical reports, papers, and patent disclosures.

What we're looking for

• Master's Degree (preferred).
• Solid working knowledge of quantum information, quantum algorithms, and practical experience running circuits using Qiskit.
• Strong mathematical foundations in linear algebra, probability, discrete mathematics, combinatorics, and computational complexity.
• Proficiency in Python and scientific libraries like NumPy and SciPy to produce reproducible, version-controlled code.
• Demonstrated research ability to frame problems, design rigorous experiments, and communicate results clearly in writing and presentations.
• Depth in at least one area including model-based optimization, machine learning, quantum-centric supercomputing, or agentic AI.
• Publications in quantum computing, optimization, or machine learning venues (preferred).