Speaker
Description
Development of quantum technology devices, and quantum technology in particular, is an arduous process, as it requires both scale and extreme accuracy.
Detailed and precise models of the devices are extremely rare, as characterization procedures of the numerous parameters comprising such models are often ad-hoc - requiring design of parameter-specific experiments and hand-coded scripts to execute and analyze the data.
The most-often used alternative are much simplified models which fail to predict gate fidelities to high accuracy and are therefore of limited utility for optimal control.
Subsequent closed-loop calibration of control pulses leave us with an equally unsatisfying situation of pulses whose precise operation we do not understand. Worst - they don't provide insight as to the causes of remaining infidelities.
Novel algorithmic and machine-learning techniques can go a long to rectify the situation. In this talk I will describe the ongoing work to develop such tools, including highly detailed TensorFlow digital twins of quantum devices, generalized model learning, optimizations based on reinforcement-learning, and automated Bayesian experiment design.
Together with more mundane quantum optimal control tools, they form a toolset which can help gain insight into the behavior of our systems, and significantly accelerate their development.
