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

• URL: http://arxiv.org/abs/2508.01690v1
• Date: Sun, 03 Aug 2025 09:50:40 GMT
• Title: First Experience with Real-Time Control Using Simulated VQC-Based Quantum Policies
• Authors: Yize Sun, Mohamad Hagog, Marc Weber, Daniel Hein, Steffen Udluft, Volker Tresp, Yunpu Ma,
• Abstract summary: This paper investigates the integration of quantum computing into offline reinforcement learning.<n>The goal is to evaluate the potential of deploying quantum architectures in real-world industrial control problems.
• Score: 22.395468970799993
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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