Stochastic Neural Networks for Quantum Devices

2026-02-24

AI Summary

This work investigates the efficient representation and optimization of stochastic neural networks on gate-model quantum computers. By systematically mapping stochastic neurons to quantum circuits, the authors construct a variety of quantum neural network architectures, including fully connected networks, Hopfield networks, restricted Boltzmann machines, autoencoders, and convolutional neural networks. These models are trained using a combination of the Kiefer–Wolfowitz algorithm and simulated annealing. Notably, the proposed quantum networks are innovatively employed as oracles within Grover’s search algorithm, yielding the first Grover-based quantum generative AI model. The study demonstrates the feasibility and effectiveness of this approach for quantum generative tasks, establishing a novel pathway toward quantum-enhanced generative modeling.

Technology Category

• Machine Learning: Quantum Machine Learning
• Computer Vision: Generative Adversarial Networks (GANs) for Vision
• Search and Optimization: Learning to Search

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

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.

Problem

• Stochastic Neural Networks
• Quantum Devices
• Quantum Neural Networks
• Gate-based Quantum Computing
• Quantum Generative AI

Innovation

• Stochastic Neural Networks
• Quantum Circuits
• Kiefer-Wolfowitz Algorithm
• Grover Oracle
• Quantum Generative AI