The Quantum Algorithms and Quantum Machine Learning Intern 2027 will join IBM Research to conduct research at the intersection of quantum computing, machine learning, and scientific computing. Working with researchers across quantum algorithms, AI, and the physical sciences, the intern will develop and evaluate quantum-classical methods for learning and simulation tasks. Key responsibilities include implementing research prototypes, benchmarking accuracy and scalability against classical methods, and analyzing where quantum resources provide a meaningful advantage. The role requires proficiency in Python, Qiskit, and scientific Python libraries, alongside a foundation in linear algebra, probability, and optimization. Candidates must apply these skills to problems in physics, chemistry, or materials science, producing technical reports, open-source code, and potential publications regarding hybrid learning workflows and quantum simulation.
Develop quantum and hybrid algorithms for scientifically meaningful learning or simulation tasks.
Implement research prototypes to benchmark accuracy, resource requirements, and scalability against classical methods.
Apply quantum and classical methods to problems in physics, chemistry, materials science, or related domains.
Analyze when quantum resources offer a meaningful advantage over classical methods while stating assumptions and costs.
Present research results through technical reports, talks, open-source code, and conference submissions.
Conduct independent research and critically analyze results to communicate technical findings clearly.
What we're looking for
Master's Degree (preferred).
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, including linear algebra, probability, optimization, or numerical methods.
Research experience in 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 with Qiskit, scientific Python libraries, machine-learning frameworks, or Git/GitHub (preferred).