QI-MPC: A Hybrid Quantum-Inspired Model Predictive Control for Learning Optimal Policies

Fuente: arXiv
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Main Authors: Khan, Muhammad Al-Zafar, Al-Karaki, Jamal
Format: Preprint
Published: 2025
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author Khan, Muhammad Al-Zafar
Al-Karaki, Jamal
author_facet Khan, Muhammad Al-Zafar
Al-Karaki, Jamal
contents In this paper, we present Quantum-Inspired Model Predictive Control (QIMPC), an approach that uses Variational Quantum Circuits (VQCs) to learn control polices in MPC problems. The viability of the approach is tested in five experiments: A target-tracking control strategy, energy-efficient building climate control, autonomous vehicular dynamics, the simple pendulum, and the compound pendulum. Three safety guarantees were established for the approach, and the experiments gave the motivation for two important theoretical results that, in essence, identify systems for which the approach works best.
format Preprint
id arxiv_https___arxiv_org_abs_2504_13041
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle QI-MPC: A Hybrid Quantum-Inspired Model Predictive Control for Learning Optimal Policies
Khan, Muhammad Al-Zafar
Al-Karaki, Jamal
Quantum Physics
Optimization and Control
In this paper, we present Quantum-Inspired Model Predictive Control (QIMPC), an approach that uses Variational Quantum Circuits (VQCs) to learn control polices in MPC problems. The viability of the approach is tested in five experiments: A target-tracking control strategy, energy-efficient building climate control, autonomous vehicular dynamics, the simple pendulum, and the compound pendulum. Three safety guarantees were established for the approach, and the experiments gave the motivation for two important theoretical results that, in essence, identify systems for which the approach works best.
title QI-MPC: A Hybrid Quantum-Inspired Model Predictive Control for Learning Optimal Policies
topic Quantum Physics
Optimization and Control
url https://arxiv.org/abs/2504.13041