Researchers have established new theoretical bounds on the resources required to learn unknown bosonic Gaussian states in quantum systems. The study proves a lower bound of \(\\Omega(n^3/\varepsilon^2)\\) for Gaussian measurements and \(\\Omega(n^2/\varepsilon^2)\\) for arbitrary measurements, concerning $n$-mode states with energy less than $E$ to $\varepsilon$ trace distance. The work also demonstrates an upper bound of $\widetilde{O}(n^2/\varepsilon^2)$ for pure or passive Gaussian states, highlighting that non-Gaussian measurements are necessary for optimal learning of passive states. These findings advance quantum learning theory and have implications for quantum sensing and benchmarking. AI
RANK_REASON Academic paper detailing theoretical findings in quantum physics. [lever_c_demoted from research: ic=1 ai=0.1]
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