A Kerr kernel quantum learning machine

TL;DR

This paper proposes a novel quantum kernel implementation using Kerr nonlinearities in superconducting circuits, enabling efficient high-dimensional quantum machine learning without relying on qubits or quantum circuits.

Contribution

It introduces an analog quantum kernel scheme based on Kerr coupled modes, distinct from traditional qubit-based quantum computing approaches.

Findings

Proposes a Kerr nonlinearities-based quantum kernel implementation.

Utilizes superconducting quantum circuits without qubits.

Enables high-dimensional quantum feature space processing.

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 matrix with indisputably nonclassical elements is desirable provided it can be generated efficiently in a particular physical machine. 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. We propose a quantum hardware kernel implementation scheme based on superconducting quantum circuits. The scheme does not use qubits or quantum circuits but rather exploits the analogue features of Kerr coupled modes. Our approach is more akin to the growing number of analog machine learning schemes based on sampling quantum probabilities directly in an engineered device by stochastic quantum control.