Structured Unitary Tensor Network Representations for Circuit-Efficient Quantum Data Encoding
Read the original on arXiv Machine Learning →The paper introduces Tensor Network-based Quantum Data Encoding (TNQE), a framework that uses structured unitary tensor network representations to encode classical data into quantum states efficiently. TNQE decomposes each input into tensor cores and compiles them into encoding circuits via two core-to-circuit strategies, with learnable block unitaries ensuring trainability while maintaining unitarity. The method achieves significantly shallower circuits—down to 0.04× the depth of amplitude encoding—and scales to high‑resolution images, demonstrating practical feasibility on real quantum hardware.
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