19 citations · 133 across the 21 of their papers we have counts for
22 papers
Quantum-Assisted Correlation Clustering
Antonio Macaluso, Supreeth Mysore Venkatesh, Diego Arenas +2
This work introduces a hybrid quantum-classical method to correlation clustering, a graph-based unsupervised learning task that seeks to partition the nodes in a graph based on pai…
Quantum computing and artificial intelligence: status and perspectives
Giovanni Acampora, Andris Ambainis, Natalia Ares +36
This white paper discusses and explores the various points of intersection between quantum computing and artificial intelligence (AI). It describes how quantum computing could supp…
i-QLS: Quantum-supported Algorithm for Least Squares Optimization in Non-Linear Regression
Supreeth Mysore Venkatesh, Antonio Macaluso, Diego Arenas +2
We propose an iterative quantum-assisted least squares (i-QLS) optimization method that leverages quantum annealing to overcome the scalability and precision limitations of prior q…
Quantum Artificial Intelligence: A Brief Survey
Matthias Klusch, Jörg Lässig, Daniel Müssig +2
Quantum Artificial Intelligence (QAI) is the intersection of quantum computing and AI, a technological synergy with expected significant benefits for both. In this paper, we provid…
Quantum Annealing-Based Algorithm for Efficient Coalition Formation Among LEO Satellites
Supreeth Mysore Venkatesh, Antonio Macaluso, Marlon Nuske +2
The increasing number of Low Earth Orbit (LEO) satellites, driven by lower manufacturing and launch costs, is proving invaluable for Earth observation missions and low-latency inte…
Quantum Supervised Learning
Antonio Macaluso
Recent advancements in quantum computing have positioned it as a prospective solution for tackling intricate computational challenges, with supervised learning emerging as a promis…
Qubit-efficient Variational Quantum Algorithms for Image Segmentation
Supreeth Mysore Venkatesh, Antonio Macaluso, Marlon Nuske +2
Quantum computing is expected to transform a range of computational tasks beyond the reach of classical algorithms. In this work, we examine the application of variational quantum…
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-…
GCS-Q: Quantum Graph Coalition Structure Generation
Supreeth Mysore Venkatesh, Antonio Macaluso, Matthias Klusch
The problem of generating an optimal coalition structure for a given coalition game of rational agents is to find a partition that maximizes their social welfare and is known to be…
BILP-Q: Quantum Coalition Structure Generation
Supreeth Mysore Venkatesh, Antonio Macaluso, Matthias Klusch
Quantum AI is an emerging field that uses quantum computing to solve typical complex problems in AI. In this work, we propose BILP-Q, the first-ever general quantum approach for so…
A Deep Learning Method for AGILE-GRID GRB Detection
N. Parmiggiani, A. Bulgarelli, V. Fioretti +7
The follow-up of external science alerts received from Gamma-Ray Bursts (GRB) and Gravitational Waves (GW) detectors is one of the AGILE Team's current major activities. The AGILE…
Self-Supervised Bernoulli Autoencoders for Semi-Supervised Hashing
Ricardo Ñanculef, Francisco Mena, Antonio Macaluso +2
Semantic hashing is an emerging technique for large-scale similarity search based on representing high-dimensional data using similarity-preserving binary codes used for efficient…
Quantum Ensemble for Classification
Antonio Macaluso, Luca Clissa, Stefano Lodi +1
A powerful way to improve performance in machine learning is to construct an ensemble that combines the predictions of multiple models. Ensemble methods are often much more accurat…