Nolan J. Coble

Applied Mathematics PhD Student


I am a third year PhD student at the Joint Center for Quantum Information and Computer Science (QuICS) at the University of Maryland, College Park. I graduated from SUNY Brockport in 2020 majoring in mathematics and physics.

My research interests are in the application of pure mathematics to quantum information and theoretical computer science. Recently, I have been interested in the use of error-correcting codes in Hamiltonian complexity and developing new quantum error-correcting codes.



Doctor of Philosophy

2020 — present

University of Maryland, College Park, MD

Applied Mathematics & Statistics, and Scientific Computation

Bachelor of Science

2016 — 2020

SUNY Brockport, Brockport, NY

Physics and Mathematics Major

Research Programs

Quantum Computing Summer School Fellowship

Summer 2021

Los Alamos National Laboratory, Los Alamos, NM

  • Advised by Dr. Yigit Subasi

Training and Research Experiences in Nonlinear Dynamics (TREND) REU

Summer 2019

University of Maryland, College Park, MD

Photonics REU

Summer 2018

University or Rochester, Rochester, NY



  1. 2022. Parallel Machine Learning for Forecasting the Dynamics of Complex Networks, Keshav Srinivasan\(^\star\), Nolan J. Coble\(^\star\), Joy Hamlin\(^\star\), Thomas Antonsen, Edward Ott, and Michelle Girven. Phys. Rev. Lett. 128, 164101. [pdf] or [arXiv:2108.12129] or [APS]
  2. 2022. Spectra of Quaternion Unit Gain Graphs, Francesco Belardo, Maurizio Brunetti, Nolan J. Coble, Nathan Reff, and Howard Skogman. Linear Algebra and its Applications. 632, p.15-49. [ScienceDirect]
  3. 2021. On nonlinear transformations in quantum computation, Zoë Holmes\(^\star\), Nolan J. Coble, Andrew T. Sornborger, and Yiğit Subaşı\(^\star\). Preprint. [pdf] or [arXiv:2108.12129]
  4. 2021. Quasi-polynomial time approximation of output probabilities of geometrically-local, shallow quantum circuits, Nolan J. Coble\(^\star\) and Matthew Coudron\(^\star\). Conference on Quantum Information Processing (QIP 2021). Symposium on Foundations of Computer Science (FOCS 2021). [pdf] or [arXiv:2012.05460]
  5. 2020. A Reservoir Computing Scheme for Multi-class Classification, Nolan J. Coble\(^\star\) and Ning Yu. 2020 ACM Southeast Conference, Tampa, FL. [pdf] or [ACM Digital Library]
\(^\star\) denotes a main contributor


  1. 2021. Divide-and-conquer method for approximating output probabilities of geometrically-local, shallow-depth quantum circuits, presented to The IQC-QuICS Math and Computer Science Seminar. [pdf] and [Seminar Website]
  2. 2020. Developing a Parallel Machine Learning Approach for Network Predictions, presented to SUNY Brockport physics department. [pdf]
  3. 2020. Poster: Predicting Oscillatory Systems with Machine Learning, presented at SUNY Brockport Scholars Day. [pdf] or [SUNY Brockport Digital Commons]
  4. 2019. Poster: Parallel Machine Learning Prediction of Network Dynamics, presented at University of Maryland TREND REU Research Fair. [pdf]

Contact Me

Feel free to contact me about anything! The best way to reach me is through my email below.

Email: nolanjcoble [at] gmail [dot] com