2 citations · 2 across the 7 of their papers we have counts for
8 papers
Classical Neural Networks on Quantum Devices via Tensor Network Disentanglers: A Case Study in Image Classification
Borja Aizpurua, Sukhbinder Singh, Román Orús
We address the problem of implementing bottleneck layers from classical pre-trained neural networks on a quantum computer, with the goal of exploring intrinsically quantum ansatz f…
Quantum-based QoE Optimization in Advanced Cellular Networks: Integration and Cloud Gaming Use Case
Fatma Chaouech, Javier Villegas, António Pereira +8
This work explores the integration of Quantum Machine Learning (QML) and Quantum-Inspired (QI) techniques for optimizing end-to-end (E2E) network services in telecommunication syst…
Blockchain Network Analysis using Quantum Inspired Graph Neural Networks & Ensemble Models
Luigi D'Amico, Daniel De Rosso, Ninad Dixit +6
In the rapidly evolving domain of financial technology, the detection of illicit transactions within blockchain networks remains a critical challenge, necessitating robust and inno…
Lightweight Cloud Masking Models for On-Board Inference in Hyperspectral Imaging
Mazen Ali, António Pereira, Fabio Gentile +5
Cloud and cloud shadow masking is a crucial preprocessing step in hyperspectral satellite imaging, enabling the extraction of high-quality, analysis-ready data. This study evaluate…
Accelerating Photonic Integrated Circuit Design: Traditional, ML and Quantum Methods
Alessandro Daniele Genuardi Oquendo, Ali Nadir, Tigers Jonuzi +4
Photonic Integrated Circuits (PICs) provide superior speed, bandwidth, and energy efficiency, making them ideal for communication, sensing, and quantum computing applications. Desp…
Limitations of Quantum Hardware for Molecular Energy Estimation Using VQE
Abel Carreras, David Casanova, Román Orús
Variational quantum eigensolvers (VQEs) are among the most promising quantum algorithms for solving electronic structure problems in quantum chemistry, particularly during the Nois…
Tensorization is a powerful but underexplored tool for compression and interpretability of neural networks
Safa Hamreras, Sukhbinder Singh, Román Orús
Tensorizing a neural network involves reshaping some or all of its dense weight matrices into higher-order tensors and approximating them using low-rank tensor network decompositio…
A tensor network approach for chaotic time series prediction
Rodrigo Martínez-Peña, Román Orús
Making accurate predictions of chaotic time series is a complex challenge. Reservoir computing, a neuromorphic-inspired approach, has emerged as a powerful tool for this task. It e…