Valentine Mohaugen
Quantum Machine Learning, Cybersecurity, and Cyber-Physical Systems
I'm a first-year Ph.D. student and Graduate Research Assistant in Civil Engineering at Clemson University, advised by Dr. Mashrur “Ronnie” Chowdhury. My research is in quantum machine learning, cybersecurity, and cyber-physical systems, developing quantum-enhanced learning methods for intelligent, secure transportation and critical infrastructure. 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 President of the Clemson Quantum Club.
Latest
- Oct 2026Became President of the Clemson Quantum Club
- Sep 2026Presenting a poster on MEDA, our measurement-efficient Majorana zero mode detection framework, at Clemson HPC Day
- Aug 2026Started my Ph.D. in Civil Engineering at Clemson University, researching quantum ML, cybersecurity, and cyber-physical systems in Dr. Chowdhury's group
- 2026MEDA, our paper on measurement-efficient disorder-aware detection of Majorana zero modes, accepted at IEEE QCE 2026
- May 2026Graduated from Clemson University with a B.S. in Physics and a minor in Italian Studies
Research Highlights
MZM Visibility
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.