Internship or Master’s Thesis

Bridging deep learning and quantum computing

Ref. 2025_035

Motivation

We are entering a new phase of computing in which quantum systems are becoming available at scale, enabling quantum-centric supercomputing, an approach that tightly integrates quantum and classical high-performance computing to accelerate progress toward practical quantum advantage. Within the IBM Research Zurich, we have recently demonstrated the acceleration of quantum optimization using deep learning surrogates and the enhancement of the attention mechanism using quantum computing. In this thesis/internship, we are looking forward to work with exceptionally talented students to further bridge the worlds of quantum computing and deep learning.

What You Will Do

You will explore important synergies of deep learning and quantum computing - by leveraging deep learning to enhance quantum computing applications (e.g., increase efficiency of quantum optimization solvers by developing deep learning surrogates), by using quantum computing within deep learning model design (e.g., enhancing attention mechanism based on quantum computing), or by exploring ways to integrate quantum optimization within the optimization steps of deep learning model training. Additionally, you will implement physics- or optimization- informed mechanisms that guide deep learning models in this hybrid setup and demonstrate the utility of the hybrid approach over individual deep learning and quantum approaches.

Required Qualifications

• For Master’s Thesis: Enrollment in a Master’s program in Computer Science, Electrical Engineering, Physics, Mathematics, or a related field.
• For Internship: MSc or PhD in Computer Science, Electrical Engineering, Physics, Mathematics, or a related field.
• Hands-on experience with AI/ML model training, including understanding of optimization concepts, metrics, and regularization.
• Basic understanding of quantum computing.
• Proficiency in Python and PyTorch, with solid software engineering skills (Linux, Git/GitHub, testing, reproducibility).
  

Bonus Qualifications

• Foundational understanding of classical and data-driven optimization algorithms.
• Deeper theoretical or hands-on experience is a plus (e.g., Qiskit or other SDKs).
  

Location, Timing, and Format

• Location: IBM Research Europe - Zurich, Switzerland
• Duration: Typically 6 months (Master’s Thesis) or 3-6 months (Internship)
• Supervision: Jointly supervised by researchers from the AI and Quantum research teams, with access to IBM’s high-performance compute clusters and Quantum computers.
  

Diversity

IBM is committed to diversity at the workplace. With us you will find an open, multicultural environment. Excellent flexible working arrangements enable all genders to strike the desired balance between their professional development and their personal lives.

How to apply

Please submit you application with a cover letter and curriculum vitae through the link below.

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