EEE 4423: Introduction to Quantum Computers
Undergraduate introduction to quantum information concepts, circuits, gates, algorithms, and hands-on Qiskit workflows.
Teaching
Courses emphasize intuition, rigorous modeling, Python/Jupyter workflows, Qiskit, simulations, recorded modules, and no-cost materials through FIU's quantum-computing curriculum.
Undergraduate, graduate, special-topics, senior design, and dissertation research activities in quantum engineering and computational electromagnetics, including FIU's first undergraduate/graduate quantum-computing sequence.
Undergraduate introduction to quantum information concepts, circuits, gates, algorithms, and hands-on Qiskit workflows.
Graduate-level quantum computing with deeper treatment of algorithms, noise, hardware constraints, and simulation.
Numerical methods and computational modeling for electrical devices, fields, resonators, and engineering systems.
Special topics in quantum technologies, materials, photonics, sensing, and emerging electromagnetic platforms.
Advanced topics in light–matter interaction, nanophotonic resonators, metasurfaces, polaritons, and quantum optical systems.
Mentored projects in simulation, measurement, documentation, quantum devices, metamaterials, and technical writing.
Courses use Qiskit, Python, Jupyter notebooks, simulations, recorded modules, and no-cost materials to make quantum engineering practical, reproducible, and accessible.
QTM Lab training emphasizes the daily habits that turn coursework and early projects into reproducible research.
Define the question, explain why it matters, reproduce a known baseline, choose the smallest testable step, analyze carefully, and write while the result is fresh.
Record raw data, parameters, software versions, code commits, assumptions, units, boundary conditions, calibration notes, and figure-generation scripts.
Progress is measured through evidence: validated simulations, calibrated measurements, clean figures, shared notes, draft sections, abstracts, code, datasets, and manuscripts.
Start with the motivation, outline, related work, methods, and figure plan. A strong paper grows from a clear sequence of figures and controls.
Each research meeting should answer four questions: what was done, what was found, what blocks progress, and what will happen next.
Selected public lectures and teaching videos connected to QTM Lab research and quantum engineering education.
Python, Jupyter notebooks, plotting, arrays, and computational habits for physics and engineering workflows.
Hands-on antenna simulation tutorial for electromagnetic modeling and RF design practice.
Lecture coverage of radiation concepts, antenna behavior, and practical electromagnetic interpretation.
Quantum-mechanics foundations for students entering quantum computing and quantum engineering.
Quantum-circuit elements, multi-qubit operations, and the role of entanglement in quantum information.
Algorithmic structure and circuit-level interpretation of quantum teleportation.
Invited lecture recorded for Polytechnique Montréal, connecting nonlinear platforms, Weyl semimetals, and quantum-compatible nonreciprocal response.
FIU KFSCIS seminar on scattering anomalies, embedded eigenstates, and quantum-relevant applications in resonant wave systems.