Robotic arms and quantum gates share a deep structure: both require steering a dynamical system along an optimal path. We port industry-tested trajectory optimization methods from robotics into quantum control.

High-fidelity gate synthesis, pulse optimization, and open-loop control for quantum hardware — with fine-grained control over constraints, costs, and solver backends.

Solve quantum optimal control problems using direct trajectory optimization. Define your system, constraints, and objectives — Piccolo handles the rest.

→ Use this first. High-level interface for gate synthesis & pulse optimization.

Low-level abstractions for direct collocation and shooting methods. Build custom trajectory optimization pipelines with full control over discretization and solver setup.

→ Use this when you need to go beyond Piccolo's defaults.

A lightweight data structure for working with trajectories keyed by user-defined component names. Makes trajectory data ergonomic to inspect, slice, and pass between solvers.

→ Use this for trajectory post-processing or standalone trajectory handling.

Unified documentation tooling across the Piccolo ecosystem. Keeps cross-package docs consistent and co-located.

→ For contributors working across multiple Harmoniqs packages.

🎓 Harvard University

Systematically benchmarking the expressivity of neutral atom arrays by engineering effective quantum interactions under various control regimes.

🎓 Stanford University

Designing and testing new hardware by developing novel integrated controls for complex quantum systems.

⚡ Infleqtion

Applying novel quantum optimal control approaches to design integrated solutions for quantum sensing applications.

🎓 NYU

Driving innovation in bosonic encodings for quantum error correction, navigating large state spaces with fine-tuned control.

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📖 Docs & Examples
Full tutorials and API reference at docs.harmoniqs.co

👋 Work With Us
Interested in collaborating? Reach out to us