Advanced Control Calibration for NISQ QPUs and Quantum Devices - Theory and Experiments

As the era of NISQ is dawning, calibration and characterization of QPUs is becoming an increasingly complex task due to the growing amount of qubits and high fidelity requirements. To tackle this problem, we developed a machine learning driven approach for Combined Calibration and Characterization procedure, C^3. This talk focuses on the gate calibration task, where optimization algorithms are used to find the best pulse parameter values in a multidimensional space.
We present a strategy which is capable of optimizing dozens of parameters, optimizing both an entangling gate and the single qubit gates of the two qubits involved, and to do so significantly faster than previously possible.
To succeed in this endeavor we engaged in an in-depth study of the multitudes of gradient-free algorithms available. The result is a unique portfolio of algorithms, for fast initial convergence and high fidelities.
Finally, we present experimental results by several labs that applied the above methodology. We show how our method takes entire gate sets all the way from 0.85 fidelities to state-of-the-art fidelities, using our open-source software.