19 citations · 133 across the 21 of their papers we have counts for
6 papers · 2 filters
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…
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…
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…