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pro vyhledávání: '"Kim, Leeseok"'
We present a randomized dynamical decoupling (DD) protocol that can improve the performance of any given deterministic DD, by using no more than two additional pulses. Our construction is implemented by probabilistically applying sequences of pulses,
Externí odkaz:
http://arxiv.org/abs/2409.18369
We propose Hamiltonian Quantum Generative Adversarial Networks (HQuGANs), to learn to generate unknown input quantum states using two competing quantum optimal controls. The game-theoretic framework of the algorithm is inspired by the success of clas
Externí odkaz:
http://arxiv.org/abs/2211.02584
Publikováno v:
Quantum Machine Intelligence 4, 3 (2022)
With the rapid advance of quantum machine learning, several proposals for the quantum-analogue of convolutional neural network (CNN) have emerged. In this work, we benchmark fully parameterized quantum convolutional neural networks (QCNNs) for classi
Externí odkaz:
http://arxiv.org/abs/2108.00661
Autor:
Kim, Leeseok1 (AUTHOR), Palacios, José Luis1 (AUTHOR) jpalacios@unm.edu
Publikováno v:
Algorithms. Feb2023, Vol. 16 Issue 2, p70. 6p.
Publikováno v:
Quantum Machine Intelligence; June 2022, Vol. 4 Issue: 1