JuliaCon 2023

Piccolo.jl: An Integrated Quantum Optimal Control Stack
07-27, 15:30–16:00 (US/Eastern), 32-082

We are introducing Piccolo.jl, an integrated quantum optimal control stack. In our recent paper, "Direct Collocation for Quantum Optimal Control", we demonstrated -- in simulation and on hardware -- that our direct collocation based pulse optimization method (PICO) is a powerful alternative to existing quantum optimal control (QOC) methods. Piccolo.jl is designed to be a simple and powerful interface for utilizing this method for pulse optimization and hardware-in-the-loop control.


Piccolo.jl is a meta-package that reexports the following packages:

Please visit corresponding package links above for detailed description and documentation for each package.


I am a research associate working on Quantum Optimal Control (QOC) in the Robotics Exploration Lab at Carnegie Mellon University; I jointly work with the Schuster Lab at Stanford University, testing QOC methods on superconducting quantum devices.

I have a dual B.S. in Physics and Mathematics from Syracuse University in 2020, where my research focused on lattice quantum gravity. See my website for more about my interests.

I spend my free time reading, climbing, cooking, and attempting to teach myself Italian. My favorite book is Don Quixote.

Physics undergrad at UChicago