Generalization in quantum machine learning from few training data
Modern quantum machine learning (QML) methods involve variationally optimizing a
parameterized quantum circuit on a training data set, and subsequently making predictions …
parameterized quantum circuit on a training data set, and subsequently making predictions …
From the quantum approximate optimization algorithm to a quantum alternating operator ansatz
The next few years will be exciting as prototype universal quantum processors emerge,
enabling the implementation of a wider variety of algorithms. Of particular interest are …
enabling the implementation of a wider variety of algorithms. Of particular interest are …
Tackling the qubit map** problem for NISQ-era quantum devices
G Li, Y Ding, Y **s: Improving reliability of quantum computers by orchestrating dissimilar mistakes
Near-term quantum computers do not have the ability to perform error correction. Such Noisy
Intermediate Scale Quantum (NISQ) computers can produce incorrect output as the …
Intermediate Scale Quantum (NISQ) computers can produce incorrect output as the …