Low-Depth Bootstrapping for Matrix-Native FHE

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Fully homomorphic encryption (FHE) enables computation directly on encrypted data and is increasingly researched for privacy-preserving machine-learning inference. Such workloads are dominated by matrix multiplication, which is not represented natively by conventional vector-oriented FHE schemes. CKKS is widely used for approximate encrypted inference because it supports packed real and complex arithmetic; however, matrix multiplication generally requires specific packing formats that are tailor