Stochastic Optimization of Tree Tensor Networks

• URL: http://arxiv.org/abs/2609.00870v1
• Date: Tue, 01 Sep 2026 08:03:33 GMT
• Title: Stochastic Optimization of Tree Tensor Networks
• Abstract summary: We derive tensor tensor networks (TTNs) on both their parameter and quotient, including adaptive and learning-rate-free schemes suitable for minibatch training.<n>Using a hybrid CNN-TTN architecture, we evaluate the methods on Fashion-NIST, CIFAR10, and Imagenette.
• Score: 0.08388591755871733
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Tensor networks, originally developed for quantum many-body physics, are promising models for machine learning. We derive stochastic Riemannian optimizers for tree tensor networks (TTNs) on both their parameter and quotient manifolds, including adaptive and learning-rate-free schemes suitable for minibatch training. Using a hybrid CNN-TTN architecture, we evaluate the methods on Fashion-MNIST, CIFAR10, and Imagenette. The proposed optimizers achieve predictive performance comparable to unconstrained optimization while enabling numerically stable downstream compression.

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