Speaker
Description
Quantum control provides a powerful framework for augmenting the performance of quantum devices, improving processes from gate design and calibration through to readout. In particular, measurement-based feedback control using machine learning techniques provides a path to control large systems, by allowing autonomous agents to determine the best control solutions even in the absence of a detailed model of the system.
In this talk, we will show how this fully automated closed-loop approach can be used to design logical gates that are resilient to hardware noise and more stable over time, showing robustness to system drift up to a month. We will also show a protocol that allows for autonomous and parallel gate tune up across entire devices. Next, we present an automated hardware-informed compilation, crosstalk mitigation, and optimized gate replacement routine, as well as a highly scalable neural-network-based measurement-error-mitigation protocol in post-processing after the execution on the quantum device.
We conclude by presenting the results of the implementation of these strategies in a 16-qubit superconducting quantum computer. We demonstrate benefits that increase with system size, reaching a 9000 times enhancement in quantum algorithm performance.
