Machine-learning tools for rapid control, calibration and characterization of QPUs and other quantum devices

Abstract

The principal limiting factor in scale-up of quantum computers is not the number of qubits, but the entangling gate infidelity. Current QPU bring-up relies on a large number of tailored routines to extract individual model parameters (characterization), and the huge effort required inevitably this leads to incomplete characterization, partial insight into the sources of error, and threfore slow progress in improving gate fidelities. To rectify the situation, we provide a new integrated open-source tool-set for Control, Calibration and Characterization (C3) [1]. We present a methodology to combine these tools to find a quantitatively accurate system model, high-fidelity gates and an approximate error budget. In this talk I shall present the overall concept, and insights into future directions. Follow-up talks by Nicolas Wittler and Federico Roy will expand some of the details and walk through an example of C3 usage. [1] Wittler, N., Roy, F., Pack, K. ... & Machnes, S. (2020). An integrated tool-set for Control, Calibration and Characterization of quantum devices applied to superconducting qubits. arXiv:2009.09866 *European Commission: Marie Curie ETN QuSCo (Grant 765267) & OpenSuperQ (Grant 820363) IARPA: LogiQ Grant W911NF-16-1-0114 BMBF: VERTICONS Grant 13N14872