2023-07-27 –, 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.
overview
Piccolo.jl is a meta-package that reexports the following packages:
- QuantumCollocation.jl: set up and solve QOC problems using PICO [1].
- IterativeLearningControl.jl: utilize PICO solutions to correct model mismatch errors in situ on an experimental system.
- NamedTrajectories.jl: intuitively and efficiently store trajectory data (underlies both of the above packages)
Please visit corresponding package links above for detailed description and documentation for each package.
references
- [1] Direct Collocation for Quantum Optimal Control
- recently accepted to IEEE QCE23
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