Task-Aware Hybrid QUBO Optimization for Structured Neural Network Pruning
Read the original on arXiv Machine Learning →The paper introduces a Hybrid Quadratic Unconstrained Binary Optimization (QUBO) framework for structured neural network pruning that integrates task‑aware sensitivity metrics (first‑order Taylor and Weight‑Fisher) into the objective’s linear term and optionally uses activation similarity for quadratic interactions. It controls pruning cardinality via a binary search over a capacity incentive rather than an explicit penalty and further refines the pruning mask with a two‑stage QUBO–Tensor‑Train strategy that employs gradient‑free black‑box optimization. Experiments on SIDD image denoising with a Half‑UNet model demonstrate that this Hybrid QUBO outperforms Taylor and L1‑based QUBO baselines in PSNR and SSIM, while also revealing computational and deployment challenges of mask‑based pruning.
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