# Quantum-Classical Hybrid Information Processing via a Single Quantum System

arXiv: https://arxiv.org/abs/2209.00497 · HTML: https://mlsanity.com/paper/2209.00497/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 53 references that are in the map.
- First posted on arXiv 1 Sep 2022.

## Builds on (most cited first, 8 of 8)

- [Echo state networks are universal](https://mlsanity.com/paper/1806.00797/) (2018)
- [Physical reservoir computing -- An introductory perspective](https://mlsanity.com/paper/2005.00992/) (2020)
- [Temporal Information Processing on Noisy Quantum Computers](https://mlsanity.com/paper/2001.09498/) (2020)
- [A Unifying Framework for Information Processing in Stochastically Driven Dynamical Systems](https://mlsanity.com/paper/1906.04608/) (2019)
- [Higher-Order Quantum Reservoir Computing](https://mlsanity.com/paper/2006.08999/) (2020)
- [Learning Temporal Quantum Tomography](https://mlsanity.com/paper/2103.13973/) (2021)
- [Quantum adaptive agents with efficient long-term memories](https://mlsanity.com/paper/2108.10876/) (2021)
- [Quantum Noise-Induced Reservoir Computing](https://mlsanity.com/paper/2207.07924/) (2022)

## Built on by (most cited first, 2 of 2)

- [Quantum Grid Path Planning Using Parallel QAOA Circuits Based on Minimum Energy Principle](https://mlsanity.com/paper/2510.07413/) (2025)
- [Task-Resolved Fisher Spectroscopy for Quantum Reservoir Computing](https://mlsanity.com/paper/2609.29570/) (2026)

## Abstract (arXiv)

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.