Quantum Algorithms and Quantum Machine Learning Intern 2027

IBM Research is seeking an intern to conduct research in quantum algorithms and quantum AI. The work combines quantum computing, machine learning, and scientific computing to develop new methods for learning and simulation and to identify problems where quantum resources may provide advantage over classical counterparts. You will work in a collaborative environment with researchers across quantum algorithms, AI, and the physical sciences. 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. Enrollment in a graduate program in quantum computing, computer science, physics, applied mathematics, statistics, information theory, engineering, or a related field. Strong foundation in quantum computing and quantum algorithms, with knowledge of linear algebra, probability, optimization, or numerical methods. Research experience in at least one relevant area, such as machine learning, quantum machine learning, quantum simulation, or hybrid quantum-classical methods. Experience implementing research code in Python or a comparable scientific programming language. Ability to conduct independent research, analyze results critically, and communicate technical findings clearly. Experience developing or analyzing quantum algorithms for classical or quantum data. Familiarity with quantum machine learning methods, learning theory, or comparisons between quantum and classical models. Experience with quantum simulation, many-body physics, quantum chemistry, or related scientific applications. Experience with Qiskit, scientific Python libraries, machine-learning frameworks, or Git/GitHub. Experience running experiments on quantum processors or combining quantum measurement data with classical machine learning.