{"ID":2854019,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.16075","arxiv_id":"2510.16075","title":"Optimization of the quantization of dense neural networks from an exact QUBO formulation","abstract":"This work introduces a post-training quantization (PTQ) method for dense neural networks via a novel ADAROUND-based QUBO formulation. Using the Frobenius distance between the theoretical output and the dequantized output (before the activation function) as the objective, an explicit QUBO whose binary variables represent the rounding choice for each weight and bias is obtained. Additionally, by exploiting the structure of the coefficient QUBO matrix, the global problem can be exactly decomposed into $n$ independent subproblems of size $f+1$, which can be efficiently solved using some heuristics such as simulated annealing. The approach is evaluated on MNIST, Fashion-MNIST, EMNIST, and CIFAR-10 across integer precisions from int8 to int1 and compared with a round-to-nearest traditional quantization methodology.","short_abstract":"This work introduces a post-training quantization (PTQ) method for dense neural networks via a novel ADAROUND-based QUBO formulation. Using the Frobenius distance between the theoretical output and the dequantized output (before the activation function) as the objective, an explicit QUBO whose binary variables represen...","url_abs":"https://arxiv.org/abs/2510.16075","url_pdf":"https://arxiv.org/pdf/2510.16075v1","authors":"[\"Sergio Muñiz Subiñas\",\"Manuel L. González\",\"Jorge Ruiz Gómez\",\"Alejandro Mata Ali\",\"Jorge Martínez Martín\",\"Miguel Franco Hernando\",\"Ángel Miguel García-Vico\"]","published":"2025-10-17T09:57:28Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\"]","methods":"[]","has_code":false}