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Quantum Machine Learning

Stochastic Neural Networks for Quantum Devices

arXiv
Authors: Bodo Rosenhahn, Tobias J. Osborne, Christoph Hirche

Year

2026

Paper ID

15476

Status

Preprint

Abstract Read

~2 min

Abstract Words

95

Citations

N/A

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.

Why This Paper Matters

  • This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
  • It adds a 2026 reference point for readers tracking recent quantum research.
  • This work presents a formulation to express and optimize stochastic neural networks as quantum circuits in gate-based quantum computing.

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