Research Intern - AI and Quantum Algorithms for Optimization 2027

You will work with IBM Research scientists on the design and analysis of algorithms for quantum and quantum-centric supercomputing, with a focus on optimization. Specifically, you will:

• Design, implement, and analyze quantum, classical, and hybrid quantum-classical algorithms for optimization problems, including model-based (e.g. MILP, conic, nonlinear) and data-driven formulations.
• Develop and benchmark quantum optimization approaches - variational and non-variational methods, quantum-enhanced heuristics, and circuit-cutting or sampling-based hybrid workflows - on IBM quantum hardware and simulators.
• Contribute to quantum-centric supercomputing workflows that partition problems across QPUs and classical HPC resources, and characterize where quantum resources provide advantage.
• Establish rigorous performance baselines against state-of-the-art classical solvers, and carry out complexity, scaling, and resource-estimation analyses.
• Explore the use of AI and agentic systems for algorithm design, hyperparameter and ansatz search, code generation, and automated experiment orchestration.
• Implement research prototypes in Python (Qiskit and the broader scientific Python ecosystem), with clean, reproducible, and well-documented code.
• Present results in team meetings, contribute to technical reports, papers, and patent disclosures, and where appropriate contribute to open-source projects.