Partially unitary learning

The problem of an optimal mapping between Hilbert spaces IN of |ψ〉 and OUT of |ϕ〉 based on a set of wavefunction measurements (within a phase) ψ_{l}→ϕ_{l}, l=1,⋯,M, is formulated as an optimization problem maximizing the total fidelity ∑_{l=1}^{M}ω^{(l)}|〈ϕ_{l}|U|ψ_{l}〉|^{2} subject to probability preservation constraints on U (partial unitarity). The constructed operator U can be considered as an IN to OUT quantum channel; it is a partially unitary rectangular matrix (an isometry) of dimension dim(OUT)×dim(IN) transforming operators as A^{OUT}=UA^{IN}U^{†}. An iterative algorithm for finding the global maximum of this optimization problem is developed, and its application to a number of problems is demonstrated. A software product implementing the algorithm is available from the authors.

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Publication Details

Journal
Physical review. E
Published
2024-11-19
DOI
https://doi.org/10.1103/physreve.110.055306
Citations
6
Primary Topic
Neural Networks and Applications
Type
article
Field-Weighted Citation Impact
1.39
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article

Partially unitary learning

Vladislav Gennadievich Malyshkin, Mikhail Gennadievich Belov
6 citations
Physical review. E
Neural Networks and Applications
1.39
article

Partially unitary learning

Vladislav Gennadievich Malyshkin, Mikhail Gennadievich Belov
article en
6 citations

Abstract

The problem of an optimal mapping between Hilbert spaces IN of |ψ〉 and OUT of |ϕ〉 based on a set of wavefunction measurements (within a phase) ψ_{l}→ϕ_{l}, l=1,⋯,M, is formulated as an optimization problem maximizing the total fidelity ∑_{l=1}^{M}ω^{(l)}|〈ϕ_{l}|U|ψ_{l}〉|^{2} subject to probability preservation constraints on U (partial unitarity). The constructed operator U can be considered as an IN to OUT quantum channel; it is a partially unitary rectangular matrix (an isometry) of dimension dim(OUT)×dim(IN) transforming operators as A^{OUT}=UA^{IN}U^{†}. An iterative algorithm for finding the global maximum of this optimization problem is developed, and its application to a number of problems is demonstrated. A software product implementing the algorithm is available from the authors.

Physical review. EVol. 110(5)
Lomonosov Moscow State University (RU), Moscow State University (TJ), Ioffe Institute (RU)
Openalex Percentile: Top 15%
Neural Networks and Applications
1.39
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Partially unitary learning — Vladislav Gennadievich Malyshkin, Mikhail Gennadievich Belov · Physical review. E (2024) | TGRS Research Map | TGRS