Quantum machine learning via Kerr non-linearity - AIP2023

Abstract

Kernel methods are of current interest in quantum machine learning due to similarities with quantum computing in how they process information in high-dimensional feature (Hilbert) spaces. Kernels are believed to offer particular advantages when they cannot be computed classically, so a kernel with indisputably nonclassical elements is desirable. Kerr nonlinearities, known to be a route to universal continuous variable (CV) quantum computation, may be able to play this role for quantum machine learning. In this work we introduce a two-mode bosonic kernel with a cross-Kerr nonlinearity, and show its use as the basis for a support vector machine (SVM) classifier where classical data is encoded in quantum states. This scheme is a CV generalisation of the binary SVM classifer of IBM. We explore the unique structure of the kernel and encoded data. We then discuss possible experimental platforms in superconducting quantum circuits and quantum optics.

Carolyn Wood

Postdoctoral Scientist

Carolyn Wood is a postdoctoral researcher at the University of Queensland, in Brisbane, Australia focusing on quantum machine learning and physics at the interface between quantum mechanics and general relativity.