mlsanityData to 7 Oct 2026

Stochastic Neural Networks for Quantum Devices

arXiv 2602.22241 ↗First posted 24 Feb 2026quant-ph cs.LGcited by 0

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Abstract (arXiv)

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 artificial 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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 34 of its 82 references that are in the map. First posted on arXiv 24 Feb 2026.

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References · 34

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22 more
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