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) ψl→ϕl, l=1…M, is formulated as an optimization problem maximizing the total fidelity ∑l=1Mω(l)∣⟨ϕl∣U∣ψl⟩∣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 AOUT=UAINU†. 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.
@article{arxiv.2405.10263,
title = {Partially Unitary Learning},
author = {Mikhail Gennadievich Belov and Vladislav Gennadievich Malyshkin},
journal= {arXiv preprint arXiv:2405.10263},
year = {2024}
}
Comments
A working algorithm implementing Partially Unitary Learning arXiv:2212.14810 has been developed and generalized. See arXiv:2407.04406 for further generalization to density matrix mappings