QruiseML

Predictive modelling for quantum

Digital twin

Create a perfect replica of your quantum system

Model learning

Closes the gap between experiment & simulation

Optimal control

Obtain the perfect control parameters for maximum performance

Error budget

Quantify your sources of error, so you know exactly what to focus on

QruiseML uses advanced model learning to generate highly accurate digital twins of quantum devices.

From arbitrary experimental data, QruiseML learns system parameters and iteratively reduces the statistical distance between the output of the digital twin and the real quantum device. This allows users to accurately model their system and and explore the impact of varying parameters.

QruiseML generates an error budget - a detailed breakdown of the various device and control imperfections - enabling the user to fully understand which parameters are limiting device performance. In this way, QruiseML helps prioritise high-impact improvements and guides the design of next-generation quantum devices.