Kliesch M, Kueng R, Eisert J, Gross D (2019)
Publication Type: Journal article
Publication year: 2019
Book Volume: 3
DOI: 10.22331/q-2019-08-12-171
Quantum process tomography is the task of reconstructing unknown quantum channels from measured data. In this work, we introduce compressed sensing-based methods that facilitate the reconstruction of quantum channels of low Kraus rank. Our main contribution is the analysis of a natural measurement model for this task: We assume that data is obtained by sending pure states into the channel and measuring expectation values on the output. Neither ancillary systems nor coherent operations across multiple channel uses are required. Most previous results on compressed process reconstruction reduce the problem to quantum state tomography on the channel's Choi matrix. While this ansatz yields recovery guarantees from an essentially minimal number of measurements, physical implementations of such schemes would typically involve ancillary systems. A priori, it is unclear whether a measurement model tailored directly to quantum process tomography might require more measurements. We establish that this is not the case. Technically, we prove recovery guarantees for three different reconstruction algorithms. The reconstructions are based on a trace, diamond, and `
APA:
Kliesch, M., Kueng, R., Eisert, J., & Gross, D. (2019). Guaranteed recovery of quantum processes from few measurements. Quantum, 3. https://doi.org/10.22331/q-2019-08-12-171
MLA:
Kliesch, Martin, et al. "Guaranteed recovery of quantum processes from few measurements." Quantum 3 (2019).
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