Research Intern — AI and Quantum Algorithms for Optimization 2027

IBM Research advances quantum-centric supercomputing by combining quantum information science, artificial intelligence, and high-performance computing. The Research Intern will design, implement, analyze, and benchmark quantum and hybrid quantum-classical optimization algorithms, contribute to quantum-centric workflows, and explore AI and agentic systems for algorithm discovery and experiment orchestration.

What you'll do:

• 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

What they're looking for:

• 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 (NumPy, SciPy), 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, metaheuristics); data-driven optimization and machine learning (including learning-to-optimize and surrogate models); quantum optimization; quantum-centric supercomputing and hybrid quantum-classical algorithm development; agentic AI and LLM-based systems; theory of computation
• Master's Degree
• Publications or preprints in quantum computing, optimization, theoretical computer science, or machine learning venues
• Hands-on experience running experiments on real quantum hardware, including transpilation, error mitigation and suppression, and interpreting hardware noise
• Experience with commercial or open-source optimization solvers (CPLEX, Gurobi, MOSEK, SCIP, OR-Tools) and modeling frameworks (JuMP, Pyomo, CVXPY, DOcplex)
• Experience with HPC environments: distributed and parallel computing, GPU acceleration, job schedulers, and tensor-network or large-scale simulation tooling
• Experience building agentic AI systems — tool use, multi-agent orchestration, retrieval, and evaluation of LLM-driven workflows — particularly applied to scientific or mathematical problem solving
• Familiarity with quantum error correction, fault-tolerant algorithm design, or resource estimation

Benefits:

• Access to IBM's fleet of utility-scale quantum computers
• Access to the Qiskit software stack
• Access to IBM's classical computing infrastructure
• Opportunities to contribute to publications at leading venues
• Opportunities for open-source contributions
• Opportunities for continued collaboration with IBM Research