Intro to quantum kernels#
As part of the educational mission of quask, we have developed a series of introductory tutorials focusing on quantum kernels. These tutorials cover fundamental theoretical concepts, including what a quantum kernel is, the defining properties that characterize it, and when their usage is supported by theoretical evidence. They also delve into implementation details and practical applications.
These tutorials serve as a platform to introduce the core components of quask: the classes Operation, Ansatz, and Kernel, which model quantum kernels, the KernelFactory class used to select the backend for executing quantum kernels, and the KernelEvaluator classes that assign scores to quantum kernels based on specific criteria. The latter is an especially noteworthy family of classes, as their source code serves as a reference implementation for numerous theoretical papers.
Note
This series of tutorial are not meant to cover the basic notions of quantum computing. If you don’t know what a quantum circuit is, you can refer to the many resources available online. Alternatively, there are few books covering the needed topics. A recent and complete reference is:
Manenti Riccardo and Motta Mario. (2023). Quantum Information Science. Oxford University Press.
For an introduction to quantum machine learning you can refer to:
Schuld Maria and Petruccione Francesco. (2021). Machine learning with quantum computers. Springer.