Classical Neural Networks on Quantum Devices via Tensor Network Disentanglers: A Case Study in Image Classification

• URL: http://arxiv.org/abs/2509.06653v1
• Date: Mon, 08 Sep 2025 13:08:37 GMT
• Title: Classical Neural Networks on Quantum Devices via Tensor Network Disentanglers: A Case Study in Image Classification
• Authors: Borja Aizpurua, Sukhbinder Singh, Román Orús,
• Abstract summary: We address the problem of implementing bottleneck layers from classical pre-trained neural networks on a quantum computer.<n>We introduce two complementary algorithms for MPO disentangling.<n>We validate these methods through a proof-of-concept translation of simple classical neural networks for MNIST and CIFAR-10 image classification.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We address the problem of implementing bottleneck layers from classical pre-trained neural networks on a quantum computer, with the goal of achieving quantum advantage on near-term devices. Our approach begins with a compression step in which the target linear layer is represented as an effective matrix product operator (MPO) without degrading model performance. The MPO is then further disentangled into a more compact form. This enables a hybrid classical-quantum execution scheme, where the disentangling circuits are deployed on a quantum computer while the remainder of the network -- including the disentangled MPO -- runs on classical hardware. We introduce two complementary algorithms for MPO disentangling: (i) an explicitly disentangling variational method leveraging standard tensor-network optimization techniques, and (ii) an implicitly disentangling gradient-descent-based approach. We validate these methods through a proof-of-concept translation of simple classical neural networks for MNIST and CIFAR-10 image classification into a hybrid classical-quantum form.

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