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We present a software tool-set which combines the theoretical, optimalcontrol view of quantum devices with the practical operation andcharacterization tasks required for quantum computing. In the same framework,we perform model-based simulations to create control schemes, calibrate thesecontrols in a closed-loop with the device (or in this demo - by emulating theexperimental process) and finally improve the system model through minimizationof the mismatch between simulation and experiment, resulting in a digital twinof the device. The model based simulator is implemented using TensorFlow, fornumeric efficiency, scalability and to make use of automatic differentiation,which enables gradient-based optimization for arbitrary models and controlschemes. Optimizations are carried out with a collection of state-of-the-artalgorithms originated in the field of machine learning. All of this comes witha user-friendly Qiskit interface, which allows end-users to easily simulatetheir quantum circuits on a high-fidelity differentiable physics simulator.

Software tool-set for automated quantum system identification and device bring up