The Potential of Hybrid and Fully Quantum Machine Learning Models: A State-of-the-Art Review

Schlichte S, Sowinski C, Feng Y, Thielen N, Franke J, Risch F (2026)

Publication Type: Conference contribution

Publication year: 2026

Original Authors: Simon Schlichte, Christopher Sowinski, Yufei Feng, Nils Thielen, Jörg Franke, Florian Risch

Event location: Amberg DE

DOI: 10.1109/MIDCongress66428.2025.11547386

Abstract

AI-powered machine vision has become a widely adopted technology in fields such as quality control, healthcare, and facial recognition, making it a standard option for many applications. Despite its widespread adoption, it continues to face several challenges. The primary barriers to its broader use in certain fields include its reliance on large datasets, lengthy training times, and high computational costs. To address these challenges, researchers are exploring quantum computing, which has the potential to process complex computations more efficiently than classical methods. Quantum computing leverages principles such as superposition, entanglement, and quantum encoding, enabling exponential speedup in computation and parallel processing of high-dimensional data, such as images.Since this research field is still relatively new and multiple approaches are being explored simultaneously, this paper aims to provide the reader with a comprehensive understanding of the current state of Quantum and Hybrid Quantum Machine Learning. It analyzes the different methodologies of quantum machine learning and presents their advantages and limitations. This should enable the reader to assess future developments in this field and offer insights into potential industrial applications.

Authors with CRIS profile

Simon Schlichte Lehrstuhl für Fertigungsautomatisierung und Produktionssystematik (FAPS) Christopher Sowinski Lehrstuhl für Fertigungsautomatisierung und Produktionssystematik (FAPS) Yufei Feng Lehrstuhl für Fertigungsautomatisierung und Produktionssystematik (FAPS) Nils Thielen Lehrstuhl für Fertigungsautomatisierung und Produktionssystematik (FAPS) Jörg Franke Lehrstuhl für Fertigungsautomatisierung und Produktionssystematik (FAPS) Florian Risch Lehrstuhl für Fertigungsautomatisierung und Produktionssystematik (FAPS)

How to cite

APA:

Schlichte, S., Sowinski, C., Feng, Y., Thielen, N., Franke, J., & Risch, F. (2026). The Potential of Hybrid and Fully Quantum Machine Learning Models: A State-of-the-Art Review. In Proceedings of the 16th International MID Congress Mechatronic Integration Discourse (MID Congress). Amberg, DE.

MLA:

Schlichte, Simon, et al. "The Potential of Hybrid and Fully Quantum Machine Learning Models: A State-of-the-Art Review." Proceedings of the 16th International MID Congress Mechatronic Integration Discourse (MID Congress), Amberg 2026.

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