Sample-based training of quantum generative models

• URL: http://arxiv.org/abs/2511.11802v1
• Date: Fri, 14 Nov 2025 19:00:02 GMT
• Title: Sample-based training of quantum generative models
• Authors: Maria Demidik, Cenk Tüysüz, Michele Grossi, Karl Jansen,
• Abstract summary: We introduce a training framework that extends the principle of contrastive divergence to quantum models.<n>By deriving the circuit structure and providing a general recipe for constructing it, we obtain quantum circuits that generate the samples required for parameter updates.
• Score: 1.3521721488318912
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quantum computers can efficiently sample from probability distributions that are believed to be classically intractable, providing a foundation for quantum generative modeling. However, practical training of such models remains challenging, as gradient evaluation via the parameter-shift rule scales linearly with the number of parameters and requires repeated expectation-value estimation under finite-shot noise. We introduce a training framework that extends the principle of contrastive divergence to quantum models. By deriving the circuit structure and providing a general recipe for constructing it, we obtain quantum circuits that generate the samples required for parameter updates, yielding constant scaling with respect to the cost of a forward pass, analogous to backpropagation in classical neural networks. Numerical results demonstrate that it attains comparable accuracy to likelihood-based optimization while requiring substantially fewer samples. The framework thereby establishes a scalable route to training expressive quantum generative models directly on quantum hardware.

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