NICHOLAS PARRILLA

Quantum Technology, SJSU

NICHOLAS PARRILLA

Quantum Technology, SJSU

 Nicholas ParrillaQuantum engineering is an exciting emerging and interdisciplinary field. This traineeship will allow me to sharpen my skillset through a comprehensive curriculum in physics and mathematics while contributing novel ideas in quantum information science by conducting research with some of the leaders in the field. While my experience during/after my undergraduate studies was focused on engineering and applications of machine learning, I am hoping to take a new angle for my research, applying my passion for high-energy physics to the field of quantum technology. My research is based on the idea that black holes are an excellent arena to rigorously investigate phenomena such as quantum entanglement entropy, quantum chaos, and emergent phenomena. I believe this integrative approach to quantum information science can shed light on many open problems and provide new insights as the field moves forward.

Aside from my academic interests, I enjoy outdoor activities including hiking, climbing, and most recently, skiing, and can often be found walking around with my dog, Hazel.

Education

BS, Physics, Case Western Reserve University, Cleveland, OH

Research Interests

  • Holographic Dualities
  • Tensor Networks
  • Quantum Many-Body Physics
  • Machine Learning/ AI

Current Project

 Investigating connections between replica wormhole calculations of black hole radiation entropy and probes of quantum chaos using ideas from random matrix theory and free probability

Mentors

Dr. Curtis Asplund, SJSU Physics

Publications

  •  Pei Yao Li, Nicholas A. Parrilla, Marco Salathe, Tenzing H. Joshi, Reynold J. Cooper, Ki Park, Asa V. Sudderth, Semi-automatic image annotation using 3D LiDAR projections and depth camera data, Annals of Nuclear Energy, Volume 213, 2025, 111080, ISSN 0306-4549, https://doi.org/10.1016/j.anucene.2024.111080.
  • Karimi, Ahmad Maroof, Fada, Justin S., Parrilla, Nicholas A., Pierce, Benjamin G., Koyutürk, Mehmet, French, Roger H., and Braid, Jennifer L., 2020, Generalized and Mechanistic PV Module Performance Prediction From Computer Vision and Machine Learning on Electroluminescence Images. IEEE Journal of Photovoltaics, 10(3), 878–887, https://doi.org/10.1109/JPHOTOV.2020.2973448.