AI and the World's Hard Problems · Quantum physics & quantum computing · arXiv · 2026-10-23
Structured Unitary Tensor Network Representations for Circuit-Efficient Quantum Data Encoding
Encoding classical data into quantum states is a key bottleneck in quantum machine learning, since many common methods need deep circuits and substantial quantum resources. The authors propose TNQE, a framework that decomposes data into structured unitary tensor networks and compiles them into encoding circuits using learnable block unitaries. Across benchmarks it achieves circuits as shallow as 0.04 times the depth of amplitude encoding, scales to 256×256 images, and shows practical feasibility on real quantum hardware.