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 are now open for 2022 and 2023. Please inquire at [email protected] 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
Theory of circuit Quantum Electrodynamics Systems
Science and technology communication
Software Engineering for Data Analysis and High Performance Computing
Efficient Implementation of Coherent Ising Machine using FPGA
Benchmarking Stochastic Parameterized Methods with Collective Autonomous Mobility Problems
Studying noise in QAOA
Hardware-Accelerated Parallel Tempering and Non-Equilibrium Monte Carlo Methods
Decomposition Methods for Discrete Optimization Problems Leveraging Ising Solver
Science Communication and Analysis of Quantum Machine Learning and Optimization Circuits
Integrative Research in Emerging Technologies