Maison Clouatre

MIT Department: Aeronautics and Astronautics
Faculty Mentor: Prof. Moe Win
Undergraduate Institution: Mercer University
Website: LinkedIn
Research Poster

Biography


My name is Maison Clouatre (he/him), a junior electrical engineering and mathematics double major at Mercer University in Macon, Georgia. My research interests lie at the intersection of control theory, networked systems, and data-science. In particular, I venture to develop data-driven, networked control algorithms that come with theoretical guarantees on their performance. In my free time I enjoy building tube guitar amplifiers, hiking, and cooking. I am honored and thrilled to be joining the community of researchers and scholars at MIT for the summer of 2021.


2021 Abstract

Learning-Enabled Optimal Quantum Control

Maison Clouatre1,2, Mohammad Javad Khojasteh2 and Moe Win2,3
1Department of Electrical & Computer Engineering, Mercer University
2Laboratory for Information & Decision Systems,
Massachusetts Institute of Technology
3Department of Aeronautics & Astronautics, Massachusetts Institute of Technology

Optimal quantum control (OQC) has grown in importance as the fields of quantum information science and quantum computation grow.  For instance, in order to perform logical operations on quantum bits (qubits), an external control field is required to manipulate the state of the qubit.  However, OQC requires a mathematical model of the underlying quantum system, and such a model may be difficult to obtain a priori.  In this work, we propose a novel quantum tomography based Hamiltonian learning (HML) algorithm which uses physical experiments to identify the internal and control Hamiltonians which govern the dynamics of the quantum system.  Our approach involves an original optimization-based quantum process tomography algorithm defined over the complex Stiefel manifold, i.e., the set of unitary operators, to ensure physically meaningful predictions.  This approach requires less memory and is more computationally efficient than state-of-the-art quantum tomography based HML algorithms.  Once the dynamics of the system have been identified, OQC is used to generate optimal control sequences in a computer-based simulation of the quantum system using the learned model.  Once control sequences are generated, they are given to a physical quantum system in an open-loop fashion in order to preserve coherence of its state.  Both theoretical error bounds and numerical simulation support the efficacy of the proposed approach.