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New research details regret lower bound for quantum state learning

By PulseAugur Editorial · [1 sources] ·

Researchers have established a regret lower bound for online learning of quantum states using logarithmic loss. This work has implications for quantum state tomography and the development of confidence sequences for matrix means. The study introduces a quantum state tomography algorithm that is sample optimal and presents a competitive algorithm for matrix-valued random variable means. AI

RANK_REASON The cluster contains an academic paper detailing theoretical research in machine learning applied to quantum states. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

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

New research details regret lower bound for quantum state learning

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The cluster contains an academic paper detailing theoretical research in machine learning applied to quantum states. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Yen-Huan Li, Wei-Fu Tseng ·

    Regret Lower Bound for Online Learning of Quantum States with Logarithmic Loss, with Applications to Quantum State Tomography and Confidence Sequences for Matrix Means

    arXiv:2610.21273v1 Announce Type: new Abstract: We study the problem of online learning of quantum states with logarithmic loss, which is a quantum generalization of online portfolio selection and has applications to quantum state tomography, the task of estimating an unknown qua…

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