Aaron Trowbridge

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](https://github.com/aarontrowbridge/Piccolo.jl) is a meta-package that reexports the following packages:

* [QuantumCollocation.jl](https://github.com/aarontrowbridge/QuantumCollocation.jl): set up and solve QOC problems using PICO \[1\]. 
* [IterativeLearningControl.jl](https://github.com/aarontrowbridge/IterativeLearningControl.jl): utilize PICO solutions to correct model mismatch errors *in situ* on an experimental system.
* [NamedTrajectories.jl](https://github.com/aarontrowbridge/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](https://arxiv.org/abs/2305.03261)
	- recently accepted to IEEE QCE23

Aaron Trowbridge

Talks:

Piccolo.jl: An Integrated Quantum Optimal Control Stack

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