Computational Scientist
Computational Scientist

Biography

John Martyn is a computational scientist at Pacific Northwest National Laboratory and a Quantum Initiative Fellow at Harvard University, where he works on the East Coast. His research focuses on the theoretical aspects of quantum information, particularly on developing quantum and classical algorithms to simulate physical systems and solve complex computational problems.

Martyn studies quantum methods such as quantum signal processing and quantum simulation, as well as classical approaches involving tensor networks, machine-learning-based simulation, and quantum Monte Carlo. By exploring these complementary approaches, he seeks to better understand the capabilities of quantum and classical computing and identify where quantum computers may offer advantages over classical methods.

Before his current role, Martyn earned his PhD from the Massachusetts Institute of Technology in May 2025. His dissertation was titled “The Algorithmic Cookbook of Quantum Science: Quantum and Classical Recipes for Computation.” During his graduate studies, he interned at IBM Quantum and Google X. He earned his BS in physics from the University of Maryland, College Park, and previously worked as a student researcher at the California Institute of Technology.

Disciplines and Skills

  • Algorithms
  • Machine learning
  • Quantum computing
  • Quantum information science

Education

  • PhD, physics, Massachusetts Institute of Technology
  • BS, physics, University of Maryland College Park

Publications

2026

  • Goldstein-Gelb, B., K. Liu, J. M. Martyn, H. Zhou, Y. Ding, and Y. Liu. 2026. "COMPAS: A Distributed Multi-Party SWAP Test for Parallel Quantum Algorithms." In APLOS '26: Proceedings of the 31st ACM International Conference on Architectural Support for Programming Languages and Operating Systems 2: 426–441. https://doi.org/10.1145/3779212.3790143 
  • Harkness, A. R., S. Kan, C. Liu, J. M. Martyn, M. Wang, S. Xu, D. Chamaki, E. Decker, Y. Mao, L. F. Zuluaga, T. Terlaky, A. Li, and S. Stein. 2026. "FTCircuitBench: A Benchmark Suite for Fault-Tolerant Quantum Compilation and Architecture." Online Conference, New York. https://doi.org/10.48550/arXiv.2601.03185 

2025

  • Martyn, J. M., and D. Luo. 2025. "High-Accuracy Neural-Network Quantum States via Randomized Real-Time Dynamics." Machine Learning and the Physical Sciences Workshop at NeurIPS 2025.