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Semidefinite Programming for Quantum Channel Learning

arxiv.org/abs/2601.12502

quality89

Updated 3 h ago · first seen 11 Sept 2026

paper_01M294FRYHQHD6N4RRHKNK6Y3G

Published
11 Sept 2026
T1 · 3 h ago
arXiv
2601.12502
T1 · 3 h ago
Category
cs.LG
T1 · 3 h ago

Abstract

The problem of reconstructing a quantum channel from a sample of classical data is considered. When the total fidelity can be represented as a ratio of two quadratic forms (e.g., in the case of mapping a mixed state to a pure state, projective operators, unitary learning, and others), Semidefinite Programming (SDP) can be applied to solve the fidelity optimization problem with respect to the Choi matrix. A remarkable feature of SDP is that the optimization is convex, which allows the problem to be efficiently solved by a variety of numerical algorithms. We have tested several commercially available SDP solvers, all of which allowed for the reconstruction of quantum channels of different forms. A notable feature is that the Kraus rank of the obtained quantum channel typically comprises less than a few percent of its maximal possible value. This suggests that a relatively small Kraus rank quantum channel is typically sufficient to describe experimentally observed classical data. The theory was also applied to the problem of reconstructing projective operators from data. Finally, we discuss a classical computational model based on quantum channel transformation, performed and calculated on a classical computer, possibly hardware-optimized.

Authors 6

Mikhail Gennadievich Belov, Victor Victorovich Dubov, Vadim Konstantinovich Ivanov, Alexander Yurievich Maslov, Olga Vladimirovna Proshina, Vladislav Gennadievich Malyshkin

Specification

Official page

Source:arXiv (Atom API + RSS)T1observed 3 h agohigh

Arxiv announce type
replace

Source:arXiv (Atom API + RSS)T1observed 3 h agohigh

arXiv id
2601.12502

Source:arXiv (Atom API + RSS)T1observed 3 h agohigh

Categories
cs.LG, cs.NA, math.NA, quant-ph

Source:arXiv (Atom API + RSS)T1observed 3 h agohigh

DOI
10.1103/kdl5-smrh

Source:arXiv (Atom API + RSS)T1observed 3 h agohigh

PDF

Source:arXiv (Atom API + RSS)T1observed 3 h agohigh

Primary category
cs.LG

Source:arXiv (Atom API + RSS)T1observed 3 h agohigh

Published
11 Sept 2026

Source:arXiv (Atom API + RSS)T1observed 3 h agohigh

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Provenance

Attributed facts

10

Source tiers

T110

Freshest observation

3 h ago

Conflicts

None