Master's Thesis — Hybrid Quantum-Neural Solvers for Mixed-Integer Nonlinear Programming

Mixed-integer nonlinear programming (MINLP) problems are hard in general and remain intractable for state-of-the-art optimization solvers for many real-time applications at operational scale. In this master thesis, the candidate will investigate decomposition-based approaches that separate MINLP into discrete and continuous subproblems, addressing each with the different computational paradigms: quantum optimization for the combinatorial and neural solvers (i.e. AI surrogates) for the continuous part. Various decomposition strategies will be explored and evaluated with respect to the resulting quantum circuit characteristics (e.g., circuit depth, connectivity, and parameter efficiency) and their suitability for execution on current and future quantum hardware. The proposed concepts will be demonstrated and benchmarked for topology optimization under AC optimal power flow (AC-OPF) constraints for electric grids. This work aligns closely with IBM Research’s vision for quantum-centric supercomputing, in which quantum computing and classical AI methods are co-designed to address problems beyond the reach of either paradigm alone. The ideal candidate will bring a strong foundation in quantum computing, familiarity with machine learning for optimization, and the motivation to push the algorithmic frontier on industrially relevant hard optimization problems.

Why Join Us?

You will join a world-class research environment and collaborate closely with researchers in quantum computing, AI, and optimization. You will gain access to state-of-the-art quantum hardware and software platforms, advanced AI toolchains, and high-performance computing resources. Our environment encourages innovation and supports scientific publications and intellectual property creation.

Preferred Qualifications

• Bachelor’s degree in computer science, electrical engineering, physics, mathematics, or a related field
• Background in one or more of the following domains: quantum information theory, quantum algorithms, (combinatorial) optimization, machine learning/AI, or computational physics
• Strong programming skills in Python; experience with scientific computing and deep learning on compute clusters
• Experience with quantum software frameworks (e.g., Qiskit) is a plus
• Self-motivated, team-oriented, and eager to work on interdisciplinary research problems