Fast Disentanglement-Based Blind Quantum Source Separation and Process Tomography: A Closed-Form Solution Using a Feedback Classical Adapting Structure
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- 关键词：Blind quantum source separation ; Unsupervised unmixing ; Blind quantum process tomography ; Blind quantum system identification and inversion ; Disentanglement ; based separation principle ; Fast algorithms ; Quantum bit (qubit) ; Qubit uncoupling ; Entanglement
- 刊名：Lecture Notes in Computer Science
- 出版年：2017
- 出版时间：2017
- 年：2017
- 卷：10169
- 期：1
- 页码：438-448
- 丛书名：Latent Variable Analysis and Signal Separation
- ISBN：978-3-319-53547-0
- 卷排序：10169
文摘
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.