Due to current COVID restrictions, Feynman Academy is unable to support interns outside of the US at this time.

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 2021-2022.** Please inquire at feynmanacademy@usra.edu 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

2021

2020

2019

2018

2017

2016

All

Bibek Pokharel

University Southern California

Years participated:
2016

Studying noise in QAOA

The goal of this internship is to work on some follow up ideas from our recent arXiv:2002.11682 studying noise in QAOA. In particular of interest is how sensitive QAOA is to the initial state. We will investigate numerically how the optimal angles depend on the initial state distribution. Another related research aspect is if there can be a notion of a â€˜robust parameter setâ€™, such that one can find sets of QAOA angles so that the resulting cost is still â€˜goodâ€™ even when the initial state is not prepared precisely or if there is noise in the circuit. Time permitting, we may also try to run some experiments on IBM/Rigetti hardware to verify the noise modeling put forward in the above paper.