May 11, 2021 · In this work, we show that some problems that are classically hard to compute can be easily predicted by classical machines learning from data.
Nov 3, 2020 · In this work, we show that some problems that are classically hard to compute can be easily predicted by classical machines learning from data.
Jun 22, 2021 · This suggests that learning models made on quantum computers may be dramatically more powerful for select applications, potentially boasting ...
Abstract. The use of quantum computing for machine learning is among the most exciting prospective applications of quantum technologies.
Quantum computers promise to enhance machine learning for practical applications. Quantum machine learning for real-world data has to handle extensive amounts ...
The authors show how to tell, for a given dataset, whether a quantum model would give any prediction advantage over a classical one, and propose a projected ...
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Nov 26, 2020 · CQT Online Talks - Series: Quantum Machine Learning Journal Club Talks Speakers: Hsin-Yuan Huang, California Institute of Technology ...
Quantum entangled data, capable of encoding more information compared to classical data of the same size, is recognized for its potential to achieve quantum ...
Apr 13, 2022 · The Power of Data and Experimental Advantages in Quantum Machine Learning ; Quantum Algorithms for Eigenvalue Problems - Lin Lin. Computing ...
Jan 23, 2024 · This suggests that quantum machine learning models have the potential to offer faster computation and improved generalisation on limited data.
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