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The Quantum Intelligence Lab is a new laboratory created by ICAR with the aim to investigate and work on fundamental issues in the field of Quantum Artificial Intelligence (QAI), an emerging interdisciplinary research area at the intersection of Quantum Computing and Artificial Intelligence, two very active fields with an overwhelming speed of new developments within the last years.

The Lab brings together researchers from computer science, engineering and mathematics to contribute primarily to the theory, development and implementation of quantum enhanced algorithms and tools of artificial intelligence and machine learning, applied to bring benefits for the whole society, solving real problems in domains like precision medicine/drug discovery, smart materials and finance.

The main goals of the Lab are: i) study, assess and monitor the progress of QAI, promoting interactions between experts; ii) investigate and develop QAI algorithms capable of addressing problems of great interest, with a specific focus on hybrid quantum/classical approaches and on the paradigms of Numerical Optimization, Machine and Reinforcement Learning and Natural Language Processing; iii) study the relationships between quantum information and consciousness from the points of view of neuroscience and quantum computing; iv) test developed algorithms on recently appearing quantum devices, such as noisy intermediate-scale quantum (NISQ) computers; v) cooperate with universities, research centers and private entities interested in QAI applications; vi) disseminate its results in QAI in an accessible way for researchers and practitioners, thus paving the way for the research and industrial communities to engage with QAI.

A further key component of this Lab is the close research collaboration with quantum computing groups at different universities and with many other entities at the forefront of quantum computing.

Giuseppe Buonaiuto, Francesco Gargiulo, Giuseppe De Pietro, Massimo Esposito, Marco Pota, Best practices for portfolio optimization by quantum computing, experimented on real quantum devices, ResearchSquare preprint.

Raffaele Guarasci, Giuseppe De Pietro and Massimo Esposito, Quantum natural language processing: Challenges and opportunities, Applied Sciences, 12, p. 5651, 2022.

Mirko Consiglio, Jacopo Settino, Andrea Giordano, Carlo Mastroianni, Francesco Plastina, Salvatore Lorenzo, Sabrina Maniscalco, John Goold, and Tony J. G. Apollaro, Variational Gibbs State Preparation on NISQ devices. arXiv preprint, 2023.

Carlo Mastroianni, Luigi Scarcello, Jacopo Settino, Quantum Computing Approach for Energy Optimization in a Prosumer Community, 2022 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech), 12-15 Sep 2022.

A. Sannia, A. Giordano, N. Lo Gullo, C. Mastroianni, and F. Plastina, A hybrid classical-quantum approach to speed-up Q-learning, arXiv preprint, 2022.

Antonio Chella, Salvatore Gaglio, Maria Mannone, Giovanni Pilato, Valeria Seidita, Filippo Vella, Salvatore Zammuto, Quantum planning for swarm robotics, Robotics and Autonomous Systems, 161, p. 104362, 2023.

Giovanni Pilato and Filippo Vella, A Survey on Quantum Computing for Recommendation Systems, Information, 14(1), p. 20, 2023.

Antonio Chella, Salvatore Gaglio, Giovanni Pilato, Filippo Vella, Salvatore Zammuto, A Quantum Planner for Robot Motion, Mathematics, 10(14), p. 2475, 2022. Maria Mannone; Valeria Seidita;

Antonio Chella, Quantum RoboSound: Auditory Feedback of a Quantum-Driven Robotic Swarm, 2022 31st IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), Napoli, Italy, pp. 287-292, 2022.

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