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Introduction to QruiseML

With QruiseML, you don't just simulate your quantum system – you replicate it. From qubits to control hardware to noise sources, every component is captured so you can understand, control, and optimise every aspect of your device.

Use the digital twin to simulate system dynamics under the influence of control pulses and environmental imperfections. With model learning, the toolset bridges the gap between experiment and simulation, so you know exactly what's going on under-the-hood. Unlike black box tools that fit your experimental data but reveal nothing about your system, QruiseML uses physics-based models to extract all your parameters, even ones you maybe didn't think about!

This is where our advanced optimal control algorithms step in, fine-tuning your control pulses to push the gate fidelity to its physical limit. An error budget completes the puzzle by telling you exactly which parameters are still limiting performance and to what degree. This informs the design of your next device, accelerating progress between iterations and streamlining the path towards high fidelities.

qruise-toolset – the Python library underpinning QruiseML – offers a comprehensive suite of tools for modelling and simulating both open and closed quantum system dynamics. It supports a wide range of qubit platforms and native operations, with extensive compatibility for realistic control stacks.

Check out our example notebooks to get started! You can also learn more about QruiseML on our website.

Example notebooks

Qubit system modelling

Control stack modelling