Quantum machine learning in feature Hilbert spaces

Understand

The basic idea of quantum computing is surprisingly similar to that of kernel methods in machine learning, namely to efficiently perform computations in an intractably large Hilbert space.

• In this paper we explore some theoretical foundations of this link and show how it opens up a new avenue for the design of quantum machine learning algorithms.
• We interpret the process of encoding inputs in a quantum state as a nonlinear feature map that maps data to quantum Hilbert space.
• A quantum computer can now analyse the input data in this feature space.