19 citations · 133 across the 21 of their papers we have counts for
10 papers · 1 filter
Nav-Q: Quantum Deep Reinforcement Learning for Collision-Free Navigation of Self-Driving Cars
Akash Sinha, Antonio Macaluso, Matthias Klusch
The task of collision-free navigation (CFN) of self-driving cars is an NP-hard problem usually tackled using Deep Reinforcement Learning (DRL). While DRL methods have proven to be…
Q-Seg: Quantum Annealing-Based Unsupervised Image Segmentation
Supreeth Mysore Venkatesh, Antonio Macaluso, Marlon Nuske +2
We present Q-Seg, a novel unsupervised image segmentation method based on quantum annealing, tailored for existing quantum hardware. We formulate the pixel-wise segmentation proble…
QAL-BP: An Augmented Lagrangian Quantum Approach for Bin Packing
Lorenzo Cellini, Antonio Macaluso, Michele Lombardi
The bin packing is a well-known NP-Hard problem in the domain of artificial intelligence, posing significant challenges in finding efficient solutions. Conversely, recent advanceme…
Fluorescent Neuronal Cells v2: Multi-Task, Multi-Format Annotations for Deep Learning in Microscopy
Luca Clissa, Antonio Macaluso, Roberto Morelli +9
Fluorescent Neuronal Cells v2 is a collection of fluorescence microscopy images and the corresponding ground-truth annotations, designed to foster innovative research in the domain…
QuACS: Variational Quantum Algorithm for Coalition Structure Generation in Induced Subgraph Games
Supreeth Mysore Venkatesh, Antonio Macaluso, Matthias Klusch
Coalition Structure Generation (CSG) is an NP-Hard problem in which agents are partitioned into mutually exclusive groups to maximize their social welfare. In this work, we propose…
MAQA: A Quantum Framework for Supervised Learning
Antonio Macaluso, Matthias Klusch, Stefano Lodi +1
Quantum Machine Learning has the potential to improve traditional machine learning methods and overcome some of the main limitations imposed by the classical computing paradigm. Ho…
Quantum Splines for Non-Linear Approximations
Antonio Macaluso, Luca Clissa, Stefano Lodi +1
Quantum Computing offers a new paradigm for efficient computing and many AI applications could benefit from its potential boost in performance. However, the main limitation is the…
Enabling Non-Linear Quantum Operations through Variational Quantum Splines
Matteo Antonio Inajetovic, Filippo Orazi, Antonio Macaluso +2
The postulates of quantum mechanics impose only unitary transformations on quantum states, which is a severe limitation for quantum machine learning algorithms. Quantum Splines (QS…
Preliminary Results of a New Deep Learning Method to Detect and Localize GRBs in the AGILE/GRID Sky Maps
N. Parmiggiani, A. Bulgarelli, A. Macaluso +12
AGILE is an ASI space mission launched in 2007 to study X-ray and gamma-ray phenomena in the energy range from keV to GeV. The AGILE Team developed a real-time an…
Preliminary Results of a Deep Learning Anomaly Detection Method to Identify Gamma-Ray Bursts in the AGILE Anticoincidence System
N. Parmiggiani, A. Bulgarelli, A. Ursi +7
AGILE is a space mission launched in 2007 to study X-ray and gamma-ray astronomy. The AGILE team developed real-time analysis pipelines to detect transient phenomena such as Gamma-…