Quantum machine learning via continuous-variable cluster states and teleportation

TL;DR

This paper introduces a novel measurement-based quantum reservoir computing approach using continuous-variable cluster states and teleportation, enabling distributed quantum machine learning with internal memory for processing static and temporal data.

Contribution

It presents a new quantum reservoir computing architecture leveraging photonic continuous-variable cluster states and quantum teleportation, advancing distributed quantum machine learning.

Findings

Demonstrates internal memory in the quantum reservoir system

Shows suitability for static and temporal data processing

Validates the approach with benchmark tasks

Abstract

A new approach suitable for distributed quantum machine learning and exhibiting memory is proposed for a photonic platform. This measurement-based quantum reservoir computing takes advantage of continuous variable cluster states as the main quantum resource. Cluster states are key to several photonic quantum technologies, enabling universal quantum computing as well as quantum communication protocols. The proposed measurement-based quantum reservoir computing is based on a neural network of cluster states and local operations, where input data are encoded through measurement, thanks to quantum teleportation. In this design, measurements enable input injections, information processing and continuous monitoring for time series processing. The architecture's power and versatility are tested by performing a set of benchmark tasks showing that the protocol displays internal memory and is suitable for both static and temporal information processing without hardware modifications. This design opens the way to distributed machine learning.