Quantum Performance and Demonstrations - Intern 2027

At IBM Research, we are the innovation engine of IBM. Exploring what’s next in computing and shaping the technologies the world will rely on tomorrow. From advancing AI and hybrid cloud to pioneering practical quantum computing, we anticipate challenges and unlock new opportunities for clients, partners, and society. Working in Research means joining a team that accelerates discovery at the intersection of high-performance computing, AI, quantum, and cloud. You’ll collaborate with leading scientists, engineers, and visionaries to push boundaries and turn ideas into reality. With a culture built on curiosity, creativity, and collaboration, IBM Research offers the opportunity to grow your career while contributing to breakthroughs that transform industries and change the world. As a research intern working on quantum system performance, capabilities, and demonstrations, you will work alongside IBM researchers to develop methods for understanding, improving, and demonstrating the capabilities of quantum computing systems. Your project will be shaped in collaboration with your mentor and may draw on areas such as quantum characterization and benchmarking, error mitigation and suppression, quantum error correction, dynamic circuits, quantum simulation, and the design of experiments that probe the limits of current and emerging quantum processors. Over the course of the internship, you will: Explore research questions related to quantum system performance and capabilities, working with your mentor and the broader team to identify promising directions and develop them into concrete investigations and demonstrations. Design and execute experiments on quantum processors to characterize system performance, evaluate techniques for improving computational accuracy, or demonstrate emerging quantum capabilities. Develop quantitative methods for assessing performance, including characterization, benchmarking, error analysis, and comparisons with numerical or theoretical predictions. Explore how techniques such as error mitigation, error suppression, dynamic circuits, and error detection or correction can extend the range and quality of computations achievable on quantum hardware. Work with researchers across theory, software, and hardware to connect system-level advances to scientifically meaningful quantum computing demonstrations. Document and communicate your findings, and develop workflows and best practices that enable colleagues to apply these techniques in their own quantum experiments and demonstrations. Enrollment in an academic graduate program (Ph.D. or research- focused Master's) in Physics, Computer Science, Mathematics, Engineering, or a closely related field, with a focus on quantum computing, quantum information science, or a related area. Coursework or research experience in quantum computing, quantum information science, quantum mechanics, linear algebra, or closely related fields. Demonstrated experience performing independent research through research projects, a thesis, internships, or prior publications. Experience with scientific computing and at least one general- purpose programming language (e.g., Python), including analysis of numerical or experimental data and drawing quantitative conclusions. Publications or preprints in quantum computing, quantum information science, quantum characterization, error mitigation, quantum simulation, or experimental demonstrations on quantum hardware. Experience designing and executing experiments on quantum computing hardware, including characterization, benchmarking, dynamic decoupling, error amplification, or related studies, and interpreting experimental results. Programming experience with core scientific Python libraries (e.g., NumPy, SciPy, Matplotlib) and quantum software frameworks such as Qiskit. Familiarity with noise modeling, error mitigation or suppression, and dynamic circuits or experience with numerical simulation of quantum circuits or open quantum systems and comparison of simulations with experimental data. Familiarity with AI tooling for research.