Quantum computing
Accelerate the development of your quantum device
< 1 hour
for system calibration of a 10-qubit QPU
30+ experiments
for characterising and calibrating your QPU
digital twin
technology models your device perfectly
error budget
highlights key sources of error
What are the challenges in quantum computing?
Quantum devices require constant recalibration to consistent performance and are highly
sensitive to environmental noise, hardware imperfections, and the inherent instability of
qubits.
How can Qruise help?
Qruise’s advanced machine learning software streamlines calibration, characterisation, and
optimisation, drastically accelerating quantum device development.
Platform independent
superconducting
Rydberg atom
trapped ion
NV centres
spin qubits
Rapid qubit bring-up with QruiseOS
QruiseOS performs rapid, in-depth characterisation, calibration, and optimal control of
quantum devices. With the option to choose between more than 30 pre-defined experiments or
design your own, Qruise's rapid automated bring-up framework allows researchers to make more
progress faster, accelerating the development of next generation quantum devices.
High-fidelity digital twins with QruiseML
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. By creating an error budget, QruiseML quantifies the sources of error, allowing you to
fully understand which parameters are limiting fidelities.
