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pro vyhledávání: '"Coyle, Brian"'
Autor:
Coyle, Brian, Cherrat, El Amine, Jain, Nishant, Mathur, Natansh, Raj, Snehal, Kazdaghli, Skander, Kerenidis, Iordanis
Quantum machine learning requires powerful, flexible and efficiently trainable models to be successful in solving challenging problems. In this work, we present density quantum neural networks, a learning model incorporating randomisation over a set
Externí odkaz:
http://arxiv.org/abs/2405.20237
Autor:
Coyle, Brian
Quantum machine learning has proven to be a fruitful area in which to search for potential applications of quantum computers. This is particularly true for those available in the near term, so called noisy intermediate-scale quantum (NISQ) devices. I
Externí odkaz:
http://arxiv.org/abs/2205.09414
Information-theoretic lower bounds are often encountered in several branches of computer science, including learning theory and cryptography. In the quantum setting, Holevo's and Nayak's bounds give an estimate of the amount of classical information
Externí odkaz:
http://arxiv.org/abs/2112.06841
Publikováno v:
Quantum 6, 861 (2022)
Approximate combinatorial optimisation has emerged as one of the most promising application areas for quantum computers, particularly those in the near term. In this work, we focus on the quantum approximate optimisation algorithm (QAOA) for solving
Externí odkaz:
http://arxiv.org/abs/2111.03016
Publikováno v:
Entropy 2021, 23(10), 1281
Generative modelling is an important unsupervised task in machine learning. In this work, we study a hybrid quantum-classical approach to this task, based on the use of a quantum circuit Born machine. In particular, we consider training a quantum cir
Externí odkaz:
http://arxiv.org/abs/2110.04253