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Semidefinite Programming applied to Quantum Channel Learning

By PulseAugur Editorial · [1 sources] ·

A new paper details the application of Semidefinite Programming (SDP) to the problem of reconstructing quantum channels from classical data. The research, led by Vladislav Malyshkin, highlights that SDP can efficiently solve fidelity optimization problems when the total fidelity is a ratio of quadratic forms. The study found that a relatively small Kraus rank is typically sufficient to describe experimental data, suggesting that simpler quantum channels can often model observed phenomena. The paper also explores applying this theory to reconstruct projective operators and discusses a classical computational model for quantum channel transformation. AI

IMPACT Introduces a novel computational approach for quantum information processing, potentially impacting future AI research in quantum computing.

RANK_REASON Academic paper detailing a new method for a specific scientific problem. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Semidefinite Programming applied to Quantum Channel Learning

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Academic paper detailing a new method for a specific scientific problem. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Mikhail Gennadievich Belov, Victor Victorovich Dubov, Vadim Konstantinovich Ivanov, Alexander Yurievich Maslov, Olga Vladimirovna Proshina, Vladislav Gennadievich Malyshkin ·

    Semidefinite Programming for Quantum Channel Learning

    arXiv:2601.12502v2 Announce Type: replace Abstract: The problem of reconstructing a quantum channel from a sample of classical data is considered. When the total fidelity can be represented as a ratio of two quadratic forms (e.g., in the case of mapping a mixed state to a pure st…

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