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First Experience With Real-Time Control Using Simulated VQC-Based Quantum Policies

MCML Authors

Link to Profile Volker Tresp

Volker Tresp

Prof. Dr.

Principal Investigator

Database Systems, Data Mining and AI

Abstract

This paper investigates the integration of quantum computing into offline reinforcement learning and the deployment of the resulting quantum policy in a real-time control hardware realization of the cart-pole system. Variational Quantum Circuits (VQCs) are used to represent the policy. Classical model-based offline policy search was applied, in which a pure VQC with trainable input-output weights is used as a policy network instead of a classical multilayer perceptron. The goal is to evaluate the potential of deploying quantum architectures in real-world industrial control problems. The experimental results show that the investigated model-based offline policy search is able to generate quantum policies that can balance the hardware cart-pole. A latency analysis reveals that while local simulated execution meets real-time requirements, cloud-based quantum processing remains too slow for closed-loop control.

inproceedings

QCE 2025

IEEE International Conference on Quantum Computing and Engineering. Albuquerque, NM, USA, Aug 31-Sep 05, 2025. To be published. Preprint available.

Authors

Y. Sun • M. Hagog • M. Weber • D. Hein • S. Udluft • V. Tresp • Y. Ma

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Research Area

 A3 | Computational Models

BibTeXKey: SHW+25

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