Qiskit
IBM’s Python SDK for building, transpiling and running quantum circuits.
The most common entry point, and the one most tutorials assume.
Software people at this intersection actually reach for. Listed on merit — nothing here is paid placement.
IBM’s Python SDK for building, transpiling and running quantum circuits.
The most common entry point, and the one most tutorials assume.
The middleware layer most robotics stacks are assembled on top of.
Not a framework so much as the ecosystem everything else assumes.
A physics engine for contact-rich robotics and biomechanics simulation.
Fast contact dynamics — which is exactly the part sim-to-real strains hardest.
Google’s Python framework for writing circuits against specific hardware topologies.
Closer to the device than most SDKs — useful when connectivity matters.
Differentiable quantum programming that plugs into standard ML autodiff frameworks.
The default choice for variational work — and for meeting barren plateaus in person.
A very fast stabiliser-circuit simulator built for error-correction research.
The reason large surface-code experiments are simulable at all.
Datasets, models and tooling aimed at real-world robot learning.
Notable for treating shared datasets as the bottleneck, which they are.
Model-based design, simulation and verification tooling for robotics.
Heavier than MuJoCo and more rigorous about the model underneath.
The maintained standard API for reinforcement-learning environments.
The interface almost every RL codebase expects to find.
The Open Quantum Safe project’s library of post-quantum cryptographic algorithms.
Where to start when a fleet needs a migration plan rather than an opinion.
Managed access to quantum hardware from several vendors behind one API.
Useful for comparing platforms without signing separate agreements.
Microsoft’s cloud service for running jobs against partner quantum hardware.
The other front door to the same set of machines.
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