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Partially unitary learning.

Physical review. E·2024
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Related Experiment Video

Updated: May 27, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Quantum channel learning.

Mikhail Gennadievich Belov1, Victor Victorovich Dubov2, Alexey Vladimirovich Filimonov2

  • 1Lomonosov Moscow State University, Faculty of Mechanics and Mathematics, GSP-1, Moscow, Vorob'evy Gory 119991, Russia.

Physical Review. E
|February 20, 2025
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Summary

This study develops an iterative algorithm for optimal quantum channel mapping between density matrices, advancing beyond pure state unitary learning. The method allows distinguishing state mixtures and superpositions, applicable to quantum tomography and algorithms.

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Area of Science:

  • Quantum Information Theory
  • Quantum Computing
  • Mathematical Physics

Background:

  • Current quantum state mapping often focuses on pure states and unitary transformations.
  • Representing quantum states as density matrices and mapping as quantum channels offers a more general framework.
  • Distinguishing between superpositions and probabilistic mixtures of quantum states is crucial for advanced quantum information processing.

Purpose of the Study:

  • To formulate and solve the problem of optimal mapping between input (IN) and output (OUT) Hilbert spaces using density matrices.
  • To develop an iterative algorithm for learning quantum channels based on fidelity maximization.
  • To generalize unitary learning to mixed unitary quantum channels and density matrix mappings.

Main Methods:

  • Formulating the mapping as an optimization problem maximizing total fidelity subject to probability preservation constraints.
  • Developing an iterative algorithm for fidelity maximization when fidelity is a quadratic form.
  • Representing quantum channels as a hierarchy of mixed unitary mappings.

Main Results:

  • An iterative algorithm for learning quantum channels represented as mixed unitary channels.
  • The ability to distinguish probabilistic mixtures of states from their superpositions.
  • Demonstrated application to unitary learning of density matrix mappings and quantum channels.

Conclusions:

  • The developed approach extends unitary learning to density matrices and quantum channels.
  • The method provides a powerful tool for analyzing and learning quantum processes.
  • Potential applications include quantum inverse problems, variational quantum algorithms, and quantum tomography.