Quantum Algorithms and Quantum Machine Learning Intern 2027

As a research intern, you will develop and evaluate quantum algorithms and quantum-classical methods at the intersection of quantum computing and AI. Your project will be defined with your mentor and may involve quantum models for classical or quantum data, hybrid learning workflows, algorithms for simulating physical systems, or theory and benchmarking of quantum and classical learning methods. You will:

• Develop quantum and hybrid algorithms for scientifically meaningful learning or simulation tasks.
• Implement research prototypes and benchmark accuracy, resource requirements, and scalability against strong classical methods.
• Apply these methods to problems involving classical or quantum data in physics, chemistry, materials science, or related domains.
• Analyze when quantum resources may offer a meaningful advantage and clearly state the assumptions and costs.
• Present results through technical reports, talks, open-source code, and, where appropriate, a publication or conference submission.