English

Partially Unitary Learning

Machine Learning 2024-11-20 v2 Numerical Analysis Numerical Analysis Quantum Physics Machine Learning

Abstract

The problem of an optimal mapping between Hilbert spaces ININ of ψ\left|\psi\right\rangle and OUTOUT of ϕ\left|\phi\right\rangle based on a set of wavefunction measurements (within a phase) ψlϕl\psi_l \to \phi_l, l=1Ml=1\dots M, is formulated as an optimization problem maximizing the total fidelity l=1Mω(l)ϕlUψl2\sum_{l=1}^{M} \omega^{(l)} \left|\langle\phi_l|\mathcal{U}|\psi_l\rangle\right|^2 subject to probability preservation constraints on U\mathcal{U} (partial unitarity). The constructed operator U\mathcal{U} can be considered as an ININ to OUTOUT quantum channel; it is a partially unitary rectangular matrix (an isometry) of dimension dim(OUT)×dim(IN)\dim(OUT) \times \dim(IN) transforming operators as AOUT=UAINUA^{OUT}=\mathcal{U} A^{IN} \mathcal{U}^{\dagger}. 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.

Keywords

Cite

@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

R2 v1 2026-06-28T16:29:48.848Z