Machine learning approach to the Floquet–Lindbladian problem

Similar to its classical version, quantum Markovian evolution can be either time-discrete or time-continuous. Discrete quantum Markovian evolution is usually modeled with completely positive trace-preserving maps, while time-continuous evolution is often specified with superoperators referred to as...

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Veröffentlicht in:Chaos (Woodbury, N.Y.) N.Y.), 2022-04, Vol.32 (4), p.043117-043117
Hauptverfasser: Volokitin, V., Meyerov, I., Denisov, S.
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Meyerov, I.
Denisov, S.
description Similar to its classical version, quantum Markovian evolution can be either time-discrete or time-continuous. Discrete quantum Markovian evolution is usually modeled with completely positive trace-preserving maps, while time-continuous evolution is often specified with superoperators referred to as “Lindbladians.” Here, we address the following question: Being given a quantum map, can we find a Lindbladian that generates an evolution identical—when monitored at discrete instances of time—to the one induced by the map? It was demonstrated that the problem of getting the answer to this question can be reduced to an NP-complete (in the dimension N of the Hilbert space, the evolution takes place in) problem. We approach this question from a different perspective by considering a variety of machine learning (ML) methods and trying to estimate their potential ability to give the correct answer. Complimentarily, we use the performance of different ML methods as a tool to validate a hypothesis that the answer to the question is encoded in spectral properties of the so-called Choi matrix, which can be constructed from the given quantum map. As a test bed, we use two single-qubit models for which the answer can be obtained using the reduction procedure. The outcome of our experiment is that, for a given map, the property of being generated by a time-independent Lindbladian is encoded both in the eigenvalues and the eigenstates of the corresponding Choi matrix.
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source AIP Journals Complete; Alma/SFX Local Collection
subjects Eigenvalues
Eigenvectors
Evolution
Hilbert space
Machine learning
Qubits (quantum computing)
Questions
title Machine learning approach to the Floquet–Lindbladian problem
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