5 papers
Activation-Aware Weight Tensorization: A Calibration-Time Preconditioner for Tensor-Network LLM Compression
Alessandro Beatini, Marco Maronese, Emanuele Rodolà
Post-training tensor-network compression replaces Transformer linear layers with Tensor Train (TT) or Tree Tensor Network (TTN) operators, but standard decompositions minimize weig…
Quantum Computing for Industrial Electromagnetics: Applicability and Case Studies in Solving Maxwell's Equations
Francesco Turro, Marco Maronese, Daniele Dragoni
Computational electromagnetics plays a central role in many industrial applications but often requires substantial computational resources, particularly when fine spatial discretiz…
High-expressibility Quantum Neural Networks using only classical resources
Marco Maronese, Francesco Ferrari, Matteo Vandelli +1
Quantum neural networks (QNNs), as currently formulated, are near-term quantum machine learning architectures that leverage parameterized quantum circuits with the aim of improving…
Quantum activation functions for quantum neural networks
Marco Maronese, Claudio Destri, Enrico Prati
The field of artificial neural networks is expected to strongly benefit from recent developments of quantum computers. In particular, quantum machine learning, a class of quantum a…
A generalized primal-dual algorithm with improved convergence condition for saddle point problems
Bingsheng He, Feng Ma, Shengjie Xu +1
We generalize the well-known primal-dual algorithm proposed by Chambolle and Pock for saddle point problems, and improve the condition for ensuring its convergence. The improved co…