Biography
I am Senior Researcher at the German Research Center for Artificial Intelligence (DFKI). My primary research objective revolves around exploring the benefits of quantum algorithms for computationally hard problems in artificial intelligence. In addition to my research endeavors, I am the lecturer of the quantum artificial intelligence courses at Saarland University.
Interests
- Quantum Artificial Intelligence
- Quantum Computing
- Artificial Intelligence
Education
PhD in Computer Science and Engineering, 2021
University of Bologna
MSc in Statistical Sciences, 2017
University Bologna
Advanced Master in Data Science, 2016
Bologna Business School, University Bologna
BSc in Statistical Sciences, 2014
University Bologna
Skills
Technical
Python
Data Science
SQL
Hobbies
Reading
Science
Biking
Experience
Senior Researcher
German Research Center for Artificial Intelligence (DFKI)
Lead of the Quantum AI research unit in the IS2 team of the Agent and Simulated Reality Department (DFKI). Our research unit is committed to investigating the feasibility and potential benefits of quantum algorithms across diverse domains of Artificial Intelligence.
Researcher
German Research Center for Artificial Intelligence (DFKI)
My research primarily concentrated on developing and implementing various quantum solutions for specific AI challenges, with applications ranging from collision-free navigation of autonomous vehicles to optimizing flexible production processes in Industry 4.0.
Machine Learning SW Engineer
Menarini Silicon BioSystem
Menarini Silicon Biosystems offers unique rare cell technologies in the study of cells and their molecular characterization. As a machine learning software engineer I worked on designing and testing advanced deep learning algorithms for in vitro diagnostic (IVD) medical devices.
PhD Student
University of Bologna
My dissertation focused on the designing and implementing new quantum machine learning algorithms. It offered a dual contribution: a comprehensive quantum framework for supervised learning, and an efficient quantum algorithm for approximating non-linear activation functions.
Data Scientist
Cineca
Developing and deploying machine learning solutions for predicting university student dropout rates, and monitoring and predicting train faults utilizing HPC infrastructure.
Certifications
Certified Associate Developer - Quantum Computation using Qiskit v0.2X
An IBM Qiskit Developer is an individual who understands fundamental quantum computing concepts and is able to express them using the Qiskit open source software development kit (SDK). They have experience using the Qiskit SDK from the Python programming language to create and execute quantum computing programs on IBM Quantum computers and simulators. This individual is able to perform these tasks with little to no assistance from product.
See certificateQiskit Advocate
This badge earner has gathered a deep level of understanding with Qiskit including circuits, algorithms, simulators, qubits and noise. Through their contributions to the Qiskit and the quantum community, this individual has demonstrated an ability and commitment to educate and influence others by sharing ideas, knowledge and expertise in the field of quantum computing.
See certificate
Projects
The aim of the project is to develop a broad basis of algorithms for AI applications with optimizations on quantum computers.
Machine learning can help universities by predicting which students are at risk of dropping out, allowing institutions to take proactive measures to address this issue, which has serious economic and social consequences in Italy.
Recent Publications
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(2024).
An efficient quantum algorithm for ensemble classification using bagging.
IET Quantum Communication.
(2024).
Fluorescent Neuronal Cells v2: multi-task, multi-format annotations for deep learning in microscopy.
Scientific Data.
(2024).
QAL-BP: an augmented Lagrangian quantum approach for bin packing.
Scientific Reports.
(2023).
A Deep-learning Anomaly-detection Method to Identify Gamma-Ray Bursts in the Ratemeters of the AGILE Anticoincidence System.
The Astrophysical Journal.
(2023).
A Variational Algorithm for Quantum Single Layer Perceptron.
Machine Learning, Optimization, and Data Science.
Contact
- antonio.macaluso@dfki.de
- +49 681 85775 5242
- Stuhlsatzenhausweg 3, Saarbruecken, 66123