Erik Recio-Armengol
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View article: Single-shot quantum machine learning
Single-shot quantum machine learning Open
Quantum machine learning aims to improve learning methods through the use of quantum computers. If it is to ever realize its potential, many obstacles need to be overcome. A particularly pressing one arises at the prediction stage because …
View article: Potential and limitations of random Fourier features for dequantizing quantum machine learning
Potential and limitations of random Fourier features for dequantizing quantum machine learning Open
Quantum machine learning is arguably one of the most explored applications of near-term quantum devices. Much focus has been put on notions of variational quantum machine learning where (PQCs) are used as learning models. These PQC models…
View article: Learning complexity gradually in quantum machine learning models
Learning complexity gradually in quantum machine learning models Open
Quantum machine learning is an emergent field that continues to draw significant interest for its potential to offer improvements over classical algorithms in certain areas. However, training quantum models remains a challenging task, larg…
View article: Single-shot quantum machine learning
Single-shot quantum machine learning Open
Quantum machine learning aims to improve learning methods through the use of quantum computers. If it is to ever realize its potential, many obstacles need to be overcome. A particularly pressing one arises at the prediction stage because …