Quantum & AI Optimization Intern

At IBM Research, we build what's next in computing. Our teams work at the intersection of quantum information science, artificial intelligence, and high-performance computing — advancing quantum-centric supercomputing, where quantum processors and classical HPC resources work together to solve problems that neither can address alone.
As a research intern, you will join a team of scientists and engineers working on the algorithmic foundations of this vision: designing, analyzing, and benchmarking quantum and hybrid quantum-classical algorithms for optimization and scientific computing, and exploring how AI and agentic systems can accelerate algorithm discovery and workflow orchestration. You will have access to IBM's fleet of utility-scale quantum computers, the Qiskit software stack, and the classical computing infrastructure that surrounds them.

This is a hands-on research role. Interns are expected to contribute original technical work, and strong outcomes frequently lead to publications at leading venues, open-source contributions, and continued collaboration with IBM Research.

Your role and responsibilities

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.

Bachelor's Degree

Preferred education

Master's Degree

Required technical and professional expertise

• Quantum computing (required). Solid working knowledge of quantum information and quantum algorithms — circuit model, Hamiltonian simulation, variational and sampling-based algorithms, noise and error mitigation — with practical experience implementing and running circuits (e.g. Qiskit).
• Strong mathematical and algorithmic foundations: linear algebra, probability, discrete mathematics and combinatorics, algorithm design and analysis, and computational complexity.
• Programming proficiency in Python, including scientific and numerical libraries (Num Py, Sci Py), plus the software discipline to produce reproducible experiments and readable, version-controlled code.
• Demonstrated research ability: framing a problem precisely, designing and running rigorous experiments, and communicating results clearly in writing and in talks.
• Depth in one or more of the following, in addition to quantum: model-based optimization (linear/integer/convex/nonlinear programming, meta heuristics); data-driven optimization and machine learning (including learning-to-optimize and surrogate models); quantum optimization; quantum-centric…