Research Intern — AI and Quantum Algorithms for Optimization 2027

**Introduction** 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. **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 (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. **Preferred Technical And Professional Experience** * 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. * Open-source contributions, especially to Qiskit or related quantum or optimization libraries. * Additional programming experience in C/C++, Julia, or Rust. * Fluency in written and spoken English.