Quantum Tensor Network Neural Architecture: Exploiting Partially Symmetric Tensors in Quantum-Limited Neural Networks

This paper proposes a Quantum Tensor Network Neural Architecture that synergistically integrates quantum and classic frameworks in order to advance quantum machine-learning. The architecture increases efficiency by constraining model weights structurally. It does this using partial symmetric decompositions of tensors that are integrated into variational quanta circuits. The symmetry constraint increases tensor reconstruction accuracy, and reduces errors compared to non-constrained approaches. A hybrid quantum-classical method of training accelerates convergence, stabilizes parameter updates and establishes robust and noise-resistant performance on simulated quanta devices. Stability and scaling of the model are supported by its theoretical underpinnings including algebraic and spectrum analyses. QTNNA is a promising framework that can process high-dimensional information effectively. This approach tackles the curse of dimensionality in quantum computing, by utilizing tensor network to manage exponential complexity.

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