About
Hello, I am Joseph Dehoney, but I mostly go by Joe (or Joda depending on when we met).
I'm a Stanford M.S. CS student working at the intersection of quantum information,
quantum complexity, and learning problems in physics.
My research aims to understand which tasks in quantum information and complexity can be solved efficiently, which are provably hard, and how structural features mediate the gap between them.
Many of the problems I am drawn to take the form of learning properties of a system and then matching information-theoretic limits with concrete algorithms.
In the long run, I hope this perspective will help identify which problems are truly amenable to efficient quantum or classical methods and inform the design of reliable computational tools for science and engineering.
Research interests
My research has gone in a couple different directions in the past few years. Previously, my work was in medical machine learning. Now, in conjunction with maintaining my past ML projects, my research revolves around trying to better understand the advantages and limitations of quantum computing. The following are a few central themes that my recent research has pursued:- Verification & structure: when does mathematical structure or physical constraints allow for a quantum speedup?
- Learning in many-body settings: identifiability and complexity of learning local Hamiltonians / Gibbs states from local data.
- Noise & robustness: stabilizing estimation and optimization under realistic noise models.
Recent work
Towards QCMA containment for Kronecker coefficient positivity
Investigating whether hard instances of Kronecker positivity admit efficiently checkable witnesses; includes extending a known hardness result for certain 2-point correlation functions to a 3-point setting (with Adam Bouland & Tamara Kohler).
Learning Quantum Gibbs States Locally & Efficiently (report)
Studied identifiability of local Hamiltonian learning from thermal data via a Gibbs-state inner product, exponential clustering, and Lieb–Robinson locality; analyzed sample/time complexity and failure regimes.
Quantum optimization of MIS with Rydberg atom arrays (course presentation)
Synthesized a paper on solving Max Independent Set with programmable Rydberg-atom arrays, focusing on the MIS to blockade mapping and how control schedules (detuning/Rabi) realize analog optimization. Presented the work as a course final project in quantum control and engineering.
Background
Alongside quantum research, I build data pipelines at Stanford for multimodal biomedical and neuroimaging research datasets (automation, CI validation, Slurm/Docker/AWS workflows). I enjoy the full lifecycle: from idea → implementation → results → writeup. Futher details on the outcomes of this work can be seen in my publications section.