How can Predictive Models help?

The key advantage of a Predictive Model is its ability to provide actionable insights when it comes to identifying high-value efforts in the R&D of quantum technology devices. This is made possible through the generation of an Error Budget – a detailed break-down of the contributions of various device and control imperfections to the bottom-line performance metric. Once an Error Budget is available, the Digital Twin can be used for simulating a broad range of What-If situations to understand how changes in various system parameters will ultimately improve or degrade the performance metric.

Example: Learning Predictive Chevron Models from iSWAP data

A typical observation when plotting iSWAP-type interactions between 2 coupled qubits is the presence of distorted and asymmetric Chevron patterns arising primarily from non-ideal flux pulses. We demonstrate the simultaneous learning of both QPU parameters (e.g., coupling strength) and arbitrary control Transfer Functions (FIR + IIR) from experimental data using our Model Learning algorithms with the Digital Twin. These parameters are then validated by reproducing the distorted bipolar SNZ-type Chevrons (bottom left). Additionally, the learned Transfer Function is then inverted to obtain the pre-distortion necessary to generate pre-compensated flux pulses which then produce Chevrons with almost no asymmetry or distortions (bottom right). During the entire training process, the Digital Twin is never exposed to the bipolar data, highlighting the predictive power and data efficiency of our algorithms.