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[2602.22241] Stochastic Neural Networks for Quantum Devices
Summary
This paper explores the integration of stochastic neural networks into quantum devices, presenting a novel approach to optimize these networks as quantum circuits using various machine learning models.
Why It Matters
As quantum computing continues to evolve, understanding how to leverage machine learning techniques within quantum frameworks is crucial. This research could lead to advancements in quantum generative AI and improve the efficiency of quantum algorithms, making it a significant contribution to both fields.
Key Takeaways
- Introduces stochastic neurons and their application in quantum neural networks.
- Utilizes the Kiefer-Wolfowitz algorithm and simulated annealing for training.
- Demonstrates various neural network topologies including autoencoders and convolutional networks.
- Combines optimized networks with Grover's algorithm for quantum generative AI.
- Highlights the potential of quantum circuits in enhancing machine learning capabilities.
Quantum Physics arXiv:2602.22241 (quant-ph) [Submitted on 24 Feb 2026] Title:Stochastic Neural Networks for Quantum Devices Authors:Bodo Rosenhahn, Tobias J. Osborne, Christoph Hirche View a PDF of the paper titled Stochastic Neural Networks for Quantum Devices, by Bodo Rosenhahn and 2 other authors View PDF HTML (experimental) Abstract:This work presents a formulation to express and optimize stochastic neural networks as quantum circuits in gate-based quantum computing. Motivated by a classical perceptron, stochastic neurons are introduced and combined into a quantum neural network. The Kiefer-Wolfowitz algorithm in combination with simulated annealing is used for training the network weights. Several topologies and models are presented, including shallow fully connected networks, Hopfield Networks, Restricted Boltzmann Machines, Autoencoders and convolutional neural networks. We also demonstrate the combination of our optimized neural networks as an oracle for the Grover algorithm to realize a quantum generative AI model. Comments: Subjects: Quantum Physics (quant-ph); Machine Learning (cs.LG) Cite as: arXiv:2602.22241 [quant-ph] (or arXiv:2602.22241v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2602.22241 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Christoph Hirche [view email] [v1] Tue, 24 Feb 2026 10:16:10 UTC (4,484 KB) Full-text links: Access Paper: View a PDF of the paper titled Stochastic Neural Networks for Quant...