Nolan J. Coble

QEC Researcher

About

I am a research scientist in the quantum error correction team at IonQ. I completed my PhD in Computer Science at the Joint Center for Quantum Information and Computer Science (QuICS) at the University of Maryland, and my BS in Physics and Mathematics at SUNY Brockport.

Experience

Education

Doctor of Philosophy

2020 — 2025

University of Maryland, College Park, MD

Computer Science

Bachelor of Science

2016 — 2020

SUNY Brockport, Brockport, NY

Physics and Mathematics Major

Experience

IonQ

December 2025 — present

College Park, MD

Quantum Error Correction Researcher

Simons Institute for the Theory of Computing

Spring 2024

UC Berkeley, Berkeley, CA

Quantum Computing Summer School Fellowship

Summer 2021

Los Alamos National Laboratory, Los Alamos, NM

  • Advised by Yiğit Subaşı

Publications

Articles

  1. 2025. Correction of chain losses in trapped ion quantum computers, Nolan J. Coble, Min Ye, and Nicolas Delfosse. Preprint. [arXiv:2511.16632]
  2. 2025. Coxeter codes: Extending the Reed–Muller family, Nolan J. Coble and Alexander Barg. IEEE International Symposium on Information Theory (ISIT 2025). [arXiv:2502.14746]
  3. 2025. Geometric structure and transversal logic of quantum Reed-Muller codes, Alexander Barg, Nolan J. Coble, Dominik Hangleiter, and Christopher Kang. IEEE Transactions on Information Theory. [arXiv:2410.07595] or [T-IT]
  4. 2023. Hamiltonians whose low-energy states require Ω(n) T gates, Nolan J. Coble, Matthew Coudron, Jon Nelson, and Seyed Sajjad Nezhadi. Preprint. [arXiv:2310.01347]
  5. 2023. Local Hamiltonians with no low-energy stabilizer states, Nolan J. Coble, Matthew Coudron, Jon Nelson, and Seyed Sajjad Nezhadi. Theory of Quantum Computation, Communication and Cryptography (TQC 2023). [arXiv:2302.14755] or [Conference Version] or [Slides]
  6. 2023. Nonlinear transformations in quantum computation, Zoë Holmes, Nolan J. Coble, Andrew T. Sornborger, and Yiğit Subaşı. Phys. Rev. Research 5, 013105. [arXiv:2108.12129] or [APS]
  7. 2022. Parallel Machine Learning for Forecasting the Dynamics of Complex Networks, Keshav Srinivasan, Nolan J. Coble, Joy Hamlin, Thomas Antonsen, Edward Ott, and Michelle Girven. Phys. Rev. Lett. 128, 164101. [arXiv:2108.12129] or [APS]
  8. 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]
  9. 2021. Quasi-polynomial time approximation of output probabilities of geometrically-local, shallow quantum circuits, Nolan J. Coble and Matthew Coudron. Conference on Quantum Information Processing (QIP 2021). Symposium on Foundations of Computer Science (FOCS 2021). [arXiv:2012.05460] or [Conference Version]
  10. 2020. A Reservoir Computing Scheme for Multi-class Classification, Nolan J. Coble and Ning Yu. 2020 ACM Southeast Conference, Tampa, FL. [ACM Digital Library] or [pdf]

Talks and Posters

  1. 2025. Quantum Reed–Muller codes and their transversal logic, presented at the workshop “The Interplay Between Distance Geometry, Combinatorics, and Coding Theory” at the Brin Mathematics Research Center, College Park, MD. [pdf]
  2. 2025. Poster: Geometric structure and transversal logic of quantum Reed-Muller codes, presented at QIP 2025. [pdf]
  3. 2024. Hamiltonians whose low-energy states require Ω(n) T gates, presented to the IQC Math & CS Seminar. [pdf]
  4. 2023. Poster: Hamiltonians whose low-energy states require Ω(n) T gates, presented at the IPAM Workshop on Topology, Quantum Error Correction, and Quantum Gravity. [pdf]
  5. 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]
  6. 2020. Developing a Parallel Machine Learning Approach for Network Predictions, presented to SUNY Brockport physics department. [pdf]
  7. 2020. Poster: Predicting Oscillatory Systems with Machine Learning, presented at SUNY Brockport Scholars Day. [pdf] or [SUNY Brockport Digital Commons]
  8. 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