Valentine Mohaugen
Quantum ML/AI, Cybersecurity, and Cyber-Physical Systems
I’m a first-year Ph.D. student in Civil Engineering at Clemson University and a Graduate Research Assistant at the National Center for Transportation Cybersecurity and Resiliency (TraCR), advised by Dr. Mashrur “Ronnie” Chowdhury. My research is in quantum ML/AI, cybersecurity, and cyber-physical systems, with applications to securing critical infrastructure and transportation. I earned my B.S. in Physics with a minor in Italian Studies at Clemson in May 2026, and I’m an IBM Qiskit Advocate and a co-founder and Vice President of the Clemson Quantum Club.
Latest
- Nov 2026Attended the IBM Quantum Developer Conference 2026
- Nov 2026Served as Lead Organizer for Qiskit Fall Fest 2026 at Clemson
- Oct 2026Competed at DuQuantum 2026, Duke University’s quantum computing hackathon
- Oct 2026Competed at QHack 2026, PennyLane’s quantum computing hackathon
- Sep 2026Served as Tech Lead for SC Quantathon v3, leading technical planning and execution of the first Quantathon held at Clemson
Research Highlights
MEDA: Measurement-Efficient Majorana Zero Mode Detection
End-to-end deep learning pipeline for predicting Majorana zero mode phase diagrams from semiconductor-superconductor nanowire conductance data. Trains a modified ResNet-18 CNN autoencoder on up to 900 million synthetic Kwant-generated conductance measurements spanning a 6-dimensional parameter space with realistic disorder profiles. Features Monte Carlo dropout uncertainty quantification and supports transfer to experimental measurements.
YQuantum 2026: Cat Qubit Control Optimization (3rd Place)
Automated control-parameter tuning framework for dissipative cat qubits, built with team (Ψ)ceratops for the Alice & Bob challenge at Yale’s YQuantum 2026, where it took 3rd place. Balances competing objectives (T_Z and T_X lifetimes, target bias ratios) across realistic hardware drift using eight reward functions and five optimization algorithms.
iQuHack 2026: Circuit Optimization (1st Place)
Reed-Muller decoding-based Clifford+T circuit optimizer developed at MIT’s annual quantum hackathon. Minimizes T-gate count and circuit depth for arbitrary unitary matrices through structure-aware synthesis, pattern recognition, and phase-polynomial optimization. Winner of the Superquantum challenge (1st place), the tool provides both a CLI and Python API for fault-tolerant quantum circuit compilation, successfully optimizing all 11 challenge unitaries.