Classical Neural Networks on Quantum Devices via Tensor Network Disentanglers: A Case Study in Image Classification
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866917390527234048 |
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| author | Aizpurua, Borja Singh, Sukhbinder Orús, Román |
| author_facet | Aizpurua, Borja Singh, Sukhbinder Orús, Román |
| contents | We address the problem of implementing bottleneck layers from classical pre-trained neural networks on a quantum computer, with the goal of exploring intrinsically quantum ansatz for representing large linear layers within hybrid classical-quantum models. 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_06653 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Classical Neural Networks on Quantum Devices via Tensor Network Disentanglers: A Case Study in Image Classification Aizpurua, Borja Singh, Sukhbinder Orús, Román Quantum Physics Computational Physics We address the problem of implementing bottleneck layers from classical pre-trained neural networks on a quantum computer, with the goal of exploring intrinsically quantum ansatz for representing large linear layers within hybrid classical-quantum models. 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. |
| title | Classical Neural Networks on Quantum Devices via Tensor Network Disentanglers: A Case Study in Image Classification |
| topic | Quantum Physics Computational Physics |
| url | https://arxiv.org/abs/2509.06653 |