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A QTN built by [1], called VQTN, uses an arbitrary two-qubit unitary gate parameterization based on the connectivity and gate set design of the trapped ions computers. This is inspired by the DDQCL model [2] that uses alternating layers of single qubit rotation gates and a Global Molmer-Sorensen gate (GMS) (a sequence of XX coupling/Ising gates).
References:
[1] R. Huang, X. Tan, and Q. Xu, “Variational quantum tensor networks classifiers,” Neurocomputing, vol. 452, pp. 89–98, Sep. 2021, doi: https://doi.org/10.1016/j.neucom.2021.04.074.
[2] M. Benedetti, D. Garcia-Pintos, O. Perdomo, V. Leyton-Ortega, Y. Nam, and A. Perdomo-Ortiz, “A generative modeling approach for benchmarking and training shallow quantum circuits,” npj Quantum Information, vol. 5, no. 1, May 2019, doi: https://doi.org/10.1038/s41534-019-0157-8.
This gate parameterization can be, therefore, used to build other TNs and QCNN structures, and hence, should be added to the pipeline.
The text was updated successfully, but these errors were encountered:
A QTN built by [1], called VQTN, uses an arbitrary two-qubit unitary gate parameterization based on the connectivity and gate set design of the trapped ions computers. This is inspired by the DDQCL model [2] that uses alternating layers of single qubit rotation gates and a Global Molmer-Sorensen gate (GMS) (a sequence of XX coupling/Ising gates).
References:
This gate parameterization can be, therefore, used to build other TNs and QCNN structures, and hence, should be added to the pipeline.
The text was updated successfully, but these errors were encountered: