Quantum-Classical Hybrid Information Processing via a Single Quantum System

TL;DR

This paper introduces a quantum reservoir processor that integrates quantum and classical data processing within a single quantum system, enabling multitasking and hybrid applications like quantum tomography and classical channel equalization.

Contribution

It presents a novel quantum reservoir architecture capable of multitasking with quantum and classical inputs, including closed-loop quantum tomography and quantum depolarizing channel preparation.

Findings

Successful multitasking of quantum tomography and classical channel equalization

Closed-loop feedback enables hybrid processing even with interrupted classical input

Demonstration of quantum depolarizing channel preparation as a machine learning technique

Abstract

Current technologies in quantum-based communications bring a new integration of quantum data with classical data for hybrid processing. However, the frameworks of these technologies are restricted to a single classical or quantum task, which limits their flexibility in near-term applications. We propose a quantum reservoir processor to harness quantum dynamics in computational tasks requiring both classical and quantum inputs. This analog processor comprises a network of quantum dots in which quantum data is incident to the network and classical data is encoded via a coherent field exciting the network. We perform a multitasking application of quantum tomography and nonlinear equalization of classical channels. Interestingly, the tomography can be performed in a closed-loop manner via the feedback control of classical data. Therefore, if the classical input comes from a dynamical system, embedding this system in a closed loop enables hybrid processing even if access to the external classical input is interrupted. Finally, we demonstrate preparing quantum depolarizing channels as a novel quantum machine learning technique for quantum data processing.