Zobrazeno 1 - 10
of 3 272
pro vyhledávání: '"P., Macaluso"'
Autor:
Srinivasan, Akshaya, Geng, Alexander, Macaluso, Antonio, Kiefer-Emmanouilidis, Maximilian, Moghiseh, Ali
Exploring the potential of quantum hardware for enhancing classical and real-world applications is an ongoing challenge. This study evaluates the performance of quantum and quantum-inspired methods compared to classical models for crack segmentation.
Externí odkaz:
http://arxiv.org/abs/2410.10713
Autor:
Hua, Pu, Liu, Minghuan, Macaluso, Annabella, Lin, Yunfeng, Zhang, Weinan, Xu, Huazhe, Wang, Lirui
Robotic simulation today remains challenging to scale up due to the human efforts required to create diverse simulation tasks and scenes. Simulation-trained policies also face scalability issues as many sim-to-real methods focus on a single task. To
Externí odkaz:
http://arxiv.org/abs/2410.03645
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 provide a brief overview of what has been achieved in QAI so far and point t
Externí odkaz:
http://arxiv.org/abs/2408.10726
Autor:
Venkatesh, Supreeth Mysore, Macaluso, Antonio, Nuske, Marlon, Klusch, Matthias, Dengel, Andreas
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 internet connectivity. However, as the number of satellites increases, the
Externí odkaz:
http://arxiv.org/abs/2408.06007
Autor:
Macaluso, Antonio
Recent advancements in quantum computing have positioned it as a prospective solution for tackling intricate computational challenges, with supervised learning emerging as a promising domain for its application. Despite this potential, the field of q
Externí odkaz:
http://arxiv.org/abs/2407.17161
Offline Reinforcement Learning (ORL) is a promising approach to reduce the high sample complexity of traditional Reinforcement Learning (RL) by eliminating the need for continuous environmental interactions. ORL exploits a dataset of pre-collected tr
Externí odkaz:
http://arxiv.org/abs/2407.09415
Autor:
Yang, Hanming, Moretti, Antonio Khalil, Macaluso, Sebastian, Chlenski, Philippe, Naesseth, Christian A., Pe'er, Itsik
Reconstructing jets, which provide vital insights into the properties and histories of subatomic particles produced in high-energy collisions, is a main problem in data analyses in collider physics. This intricate task deals with estimating the laten
Externí odkaz:
http://arxiv.org/abs/2406.03242
Publikováno v:
Contemporary Physics, 1-29, 2024
Many problems intractable on classical devices could be solved by algorithms explicitly based on quantum mechanical laws, i.e. exploiting quantum information processing. As a result, increasing efforts from different fields are nowadays directed to t
Externí odkaz:
http://arxiv.org/abs/2405.21000
Autor:
Venkatesh, Supreeth Mysore, Macaluso, Antonio, Nuske, Marlon, Klusch, Matthias, Dengel, Andreas
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 algorithms (VQAs) for unsupervised image segmentation to partition ima
Externí odkaz:
http://arxiv.org/abs/2405.14405
Despite significant technological advancements, the process of programming robots for adaptive assembly remains labor-intensive, demanding expertise in multiple domains and often resulting in task-specific, inflexible code. This work explores the pot
Externí odkaz:
http://arxiv.org/abs/2405.08216