An internship at the USRA-NASA Quantum Artificial Intelligence Laboratory (QuAIL) at NASA Ames Research Center's Advanced Supercomputing Facility introduces graduate students to scientific opportunities in quantum information sciences and trains them to do research related to the most advanced quantum computing platforms. Students will receive valuable experience working on teams, undertaking projects in advanced computing, and developing quantum and classical methods to solve problems in important application or fundamental domains. The program is funded by NASA, AFRL, USRA and NSF.
Students, which preferably should be enrolled in a Ph.D. program (but motivated master's or undergrads are also considered) or have otherwise previous quantum computing research experience, are accepted to a 12-to-24 week program. Applications are open all year round. These students work in close collaboration with quantum scientists, receiving hands-on training, and undertake individualized research projects, finally resulting in a publication. Students will also participate in seminars and workshops with researchers from other organizations doing quantum research, including those from academic institutions, government laboratories, and commercial organizations. Participants receive a stipend to cover living expenses and travel during the program.
Applications for Winter-Spring 2020-2021 are now open. Please inquire at email@example.com or click on "Apply Now!" at the top of the page and fill in the form.
Potential topics (not exhaustive list):
Quantum Optimization and Sampling Algorithms (e.g. QAOA/VQE/AQC)
Benchmarking NISQ Computers
Compilation/Embedding of Quantum Algorithms
Quantum error-mitigation and correction methods
Quantum Algorithms for Materials, Chemistry, Non-equilibrium systems and High-Energy Physics
Numerical Simulation of Quantum Systems
Noise Modeling and Open Quantum Systems
Physics of Oscillator Based Computing and Coherent Optical Ising Machines
Quantum Complexity Theory
(Quantum) Machine Learning applied to Quantum Computing
Studying noise in QAOA