Automated Parameter Tuning via Heuristic Search
Conference: Machine Learning for Quantum 2021
Speaker: Timo Kötzing
Abstract: The performance of modern algorithms in computer science typically depends on a number of parameters which govern the behavior of the algorithm. Setting these parameters just right is a complex task, typically dependent on the exact area of application of the algorithm, i.e. on the data to be given to the algorithm. Traditionally, algorithm designers might play with these parameters some, using their detailed knowledge of the inner workings of the algorithm, in order to find good parameter settings. However, more and more this process can be automated by parameter tuning algorithms which explore the space of available parameter settings, evaluating possible choices along the way. One way to explore is Heuristic Search, iteratively generating more and more possible parameter settings similar to previous promising settings.
Regarding calibrating multi-qubit circuits, the general structure of the problem is the same as for tuning the parameters of algorithms: There is a set of allowed settings for the parameters, each of these settings has an associated quality (which might suffer from noise). In order to find an element of good quality in this search space, the same principles (exploration vs. exploitation trade-off, search adaptation, surrogate models, hardware in the loop, …) would also be applicable in this setting. Thus, I want to present some of these ideas to you and discuss possibilities and limitations.