2026-08-14 –, Room 3
The name means small, so we made it smaller. Piccolo.jl 1.0 consolidates five quantum optimal control packages into one -- one language, one package, just using Piccolo. A unified Julia codebase that AI coding agents can thrive in -- accelerating feature development, performance work, and letting users go from system parameters to optimized pulses naturally. We demonstrate real-world impact through robust control theory (arXiv:2602.10349) and experimental studies of universal dynamics in Rydberg arrays (arXiv:2508.19075).
Piccolo.jl is an open-source framework for quantum optimal control via direct trajectory optimization, developed by Harmoniqs.
One package to rule them all. Previously, Piccolo was a meta-package re-exporting QuantumCollocation.jl, PiccoloQuantumObjects.jl, PiccoloPlots.jl, NamedTrajectories.jl, and TrajectoryIndexingUtils.jl. Users had to navigate five repos, five sets of docs, and version compatibility across all of them. For 1.0, we pulled core functionality into Piccolo.jl itself, keeping only truly independent libraries (NamedTrajectories.jl, DirectTrajOpt.jl) as separate packages. The result: using Piccolo gives you everything from Hamiltonians to plotting.
Agent-enabled by design. Julia's single-language stack -- where the high-level API and the performance-critical internals are the same language -- turns out to be a superpower for AI-assisted development. An LLM reading Piccolo source code doesn't need to context-switch between Python glue and C++/Fortran kernels. We leaned into this by shipping structured context files and building reusable agent skills for common quantum control workflows: problem setup, physics references, testing, and demo generation. On the development side, this accelerates feature implementation and performance optimization across the stack. On the user side, coding agents can go from a gate specification to an optimized pulse with minimal human steering -- making quantum optimal control more accessible to experimentalists who think in terms of physics, not software.
Real-world impact. Piccolo's direct optimal control framework underpins two recent results. First, Kamen et al. position robustness as a first-class objective within direct, constrained optimal control, introducing a critical discretization correction to toggling-frame robustness estimators and demonstrating precise, physics-informed robust pulse design. Second, Hu et al. use the framework to experimentally demonstrate universal dynamics on Rydberg-atom arrays, synthesizing three-body interactions and topological dynamics under global control constraints.
In this talk, we demo the unified package, show how agent skills accelerate research workflows, and discuss what we have learned about designing Julia packages for the age of AI-assisted scientific computing.
Currently co-founder and CEO at Harmoniqs, a startup building Julia-based quantum optimal control and calibration software. Previously, a research associate in the Robotics Institute at Carnegie Mellon University. An avid reader, climber, runner, and Bialetti coffee drinker.