Estimating the degree of non-Markovianity using variational quantum circuits-Reference-Cited by-同舟云学术

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Estimating the degree of non-Markovianity using variational quantum circuits

Published:2023-07-17 Issue:2 Volume:5 Page:
ISSN:2524-4906
Container-title:Quantum Machine Intelligence
language:en
Short-container-title:Quantum Mach. Intell.

Author:

Dinani Hossein T.,Tancara Diego,Fanchini Felipe F.,Norambuena Ariel,Coto Raul

Publisher

Springer Science and Business Media LLC

Subject

Applied Mathematics,Artificial Intelligence,Computational Theory and Mathematics,Theoretical Computer Science,Software

Link

https://link.springer.com/content/pdf/10.1007/s42484-023-00120-5.pdf

Reference65 articles.

1. Acampora G, Schiattarella R (2021) Deep neural networks for quantum circuit mapping. Neural Comput Appl 33:13723. https://doi.org/10.1007/s00521-021-06009-3

2. Beer K, Bondarenko D, Farrelly T, Osborne TJ, Salzmann R, Scheiermann D, Wolf R (2020) Training deep quantum neural networks. Nat Comms 11:808. https://doi.org/10.1038/s41467-020-14454-2

3. Bellomo B, Lo Franco R, Compagno G (2007) Non-Markovian effects on the dynamics of entanglement. Phys Rev Lett 99(16):160502. https://doi.org/10.1103/PhysRevLett.99.160502

4. Benedetti M et al (2019) Parameterized quantum circuits as machine learning models. Quantum Sci Technol 4:043001. https://doi.org/10.1088/2058-9565/ab4eb5

5. Bergholm V et al (2020) PennyLane: Automatic differentiation of hybrid quantumclassical computations. Preprint at https://arxiv.org/abs/1811.04968v3

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