Nolan J. Coble

Articles

1. 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]
2. 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]
3. 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]
4. 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]
5. 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]
6. 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]
7. 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

1. 2024. Hamiltonians whose low-energy states require Ω(n) T gates, presented to the IQC Math & CS Seminar. [pdf]
2. 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]
3. 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]
4. 2020. Developing a Parallel Machine Learning Approach for Network Predictions, presented to SUNY Brockport physics department. [pdf]
5. 2020. Poster: Predicting Oscillatory Systems with Machine Learning, presented at SUNY Brockport Scholars Day. [pdf] or [SUNY Brockport Digital Commons]
6. 2019. Poster: Parallel Machine Learning Prediction of Network Dynamics, presented at University of Maryland TREND REU Research Fair. [pdf]