Ruth Danielle Espinosa Española

I’m a recent M.S. Electrical Engineering graduate and Data Scientist with a strong foundation in power systems, automation, and technological innovation. I focus on applying Quantum AI to optimization problems where speed and computational bottlenecks matter most.

My work bridges hybrid quantum-classical algorithms with real engineering contexts, including power flow analysis and predictive analytics for energy systems. In my thesis, I explored “Hybrid Quantum Power Flow Analysis Using Variational Quantum and Classical Optimization,” grounding the approach in power flow equations.

I’ve proven impact through leadership and research output. As Team Lead and Top 10 finalist in the “QAI Ventures Global Hackathon Series 2025,” I guided a formulation inspired by VQLS and connected it to a quantum interpretation of the Markowitz quadratic model to improve computational efficiency.

Since 2023, I’ve also served as a Graduate Research Assistant at CYCU and contributed to publishable quantum-optimization research. I led “Performance Analysis of QUBO-translated Non-maximum Suppression for Object Detection,” reformulating NMS as a QUBO model, with results showing mAP convergence (~0.375) and a best inference time improvement of 92.35% over classical NMS. I thrive in interdisciplinary environments and stay adaptable to emerging quantum and AI techniques.