Abstract
We here extend Blind (i.e. unsupervised) Quantum Source Separation and Process Tomography methods. Considering disentanglement-based approaches, we introduce associated optimization algorithms which are much faster than the previous ones, since they reduce the number of source quantum state preparations required for adaptation by a factor of \(10^3\) typically. This is achieved by unveiling the parametric forms of the optimized cost functions, which allows us to derive a closed-form solution for their optimum.
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- 1.
Considering the approach of this paper alone, \( { \gamma _c } \) is freely chosen and the simplest choice is \( { \gamma _c } = 0 \), i.e. \( \gamma _1= \gamma _4\).
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Deville, Y., Deville, A. (2017). Fast Disentanglement-Based Blind Quantum Source Separation and Process Tomography: A Closed-Form Solution Using a Feedback Classical Adapting Structure. In: Tichavský, P., Babaie-Zadeh, M., Michel, O., Thirion-Moreau, N. (eds) Latent Variable Analysis and Signal Separation. LVA/ICA 2017. Lecture Notes in Computer Science(), vol 10169. Springer, Cham. https://doi.org/10.1007/978-3-319-53547-0_41
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DOI: https://doi.org/10.1007/978-3-319-53547-0_41
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