2025-07-23 –, Main Room 6
In this talk, we will discuss recent improvements and use-cases of the Piccolo.jl meta-package for quantum optimal control. We will detail where we are at the moment and what will comprise the release of version 1.0.
Piccolo.jl sets up and solves quantum optimal control problems as nonlinear programs. It is based on the idea of quantum direct collocation. Piccolo.jl provides composable problem templates for easy implementation of common control tasks, which we will review.
This bulk of this talk will focus on the design choices that have been made in the pursuit of a version 1.0 of Piccolo.jl. We will explain the choice to break Piccolo.jl into a number of subpackages, and discuss the management of this ecosystem. We will describe the development of a Python interface, which was necessary to ease adoption by experimental groups. Furthermore, we will explain our interaction with different backend solvers, discussing Ipopt.jl, MathOptInterface.jl, MadNLP.jl, and NLPModels.jl. Finally, we will highlight a few new applications of quantum control enabled by Piccolo.jl, and showcase the default visualizations in Piccolo.jl's toolbox.
I am a research associate the Robotics Institute at Carnegie Mellon University. I work on quantum optimal control. My free time is spent climbing, running, cycling, reading, and exploring Pittsburgh.