Isaac Monteiro

Isaac Monteiro

Biography

Isaac Monteiro is a rising Junior from Lawrenceville, Georgia, majoring in Electrical
Engineering and Mathematics at Mercer University. At Mercer, Isaac is a Stamps Scholar, a
member of the Engineering Honors Program and the Mercer Robotics Club, and a participant
in a Mercer On Mission service-learning project in Cape Town, South Africa. He has held
intern positions at the Georgia Tech Research Institute and Travis Pruitt & Associates. In
Mercer’s Cyber-Physical Systems and Control Lab, he has nurtured an interest in control theory,
optimization, and electrical automation through research under the mentorship of Dr. Makhin
Thitsa. His current research investigates multi-agent systems, gradient-free optimization, and
other topics in nonlinear control. In the future, Isaac aspires to earn a PhD in engineering with
a focus on controls and automation. He hopes to research optimization strategies that contribute
to the future of energy and environmental sustainability at a national lab.

Information-Optimal Measurements for Multiparameter Quantum Estimation
Isaac Monteiro1, Maison Clouâtré2, Moe Z. Win2
1Department of Electrical and Computer Engineering, Mercer University
2Department of Aeronautics and Astronautics, Massachusetts Institute of Technology

Multiparameter quantum estimation (MQE) has many applications in sensing, metrology, and
computing. The goal of MQE is to infer a collection of parameters that are encoded into the
state of a quantum system. This is done by measuring the state using a particular measurement
apparatus and then constructing parameter estimators based on the measurement outcomes.
The quality of such estimators is limited by the amount of information contained in the
measurement outcomes. Therefore, an important engineering task is to design measurements
that optimize this information in an appropriate way. This project introduces new information
quantities for MQE that are constructed by optimizing the measurement used to interrogate
the parameterized quantum state. In particular, we scalarize the Fisher information
matrix, optimize the scalarization over physically-realizable measurements, and study the
mathematical properties of this scalarization. This work demonstrates that measurements
optimized for information extraction lead to improved capabilities in quantum metrology
and sensing. Future directions for this research include developing an adaptive measurement
framework to enable the selection of optimal measurements even when said measurements
depend on the unknown parameters. Furthermore, a case study using a particular application
(e.g., nitrogen vacancy (NV) center magnetometers) could demonstrate the framework’s
efficacy for quantum sensing.