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Research PaperResearchia:202609.23044

When are bosonic Gaussian states classical to learn?

Senrui Chen

Abstract

A fundamental question in physics is: When does classical behavior emerge from quantum systems? Bosonic Gaussian states provide a natural setting to explore this quantum-classical boundary, as they capture both the classical field behavior and the intrinsic quantum nature of light. Here, we address this problem from a learning-theoretic perspective by asking: When are bosonic Gaussian states classical to learn? That is, under what conditions (if any) can an n-mode bosonic Gaussian state be learn...

Submitted: September 23, 2026Subjects: Machine Learning; Data Science

Description / Details

A fundamental question in physics is: When does classical behavior emerge from quantum systems? Bosonic Gaussian states provide a natural setting to explore this quantum-classical boundary, as they capture both the classical field behavior and the intrinsic quantum nature of light. Here, we address this problem from a learning-theoretic perspective by asking: When are bosonic Gaussian states classical to learn? That is, under what conditions (if any) can an n-mode bosonic Gaussian state be learned with as few samples, and with operations as simple, as are needed to learn a classical 2n-variate Gaussian distribution? We establish a smooth crossover in learnability governed by the state's thermal fluctuations: - Cold Gaussian states are non-classical to learn: When the covariance matrix satisfies Σ(12+O(1n))IΣ\le(\frac12+O(\frac1n))I, i.e. close to the vacuum covariance, tomography under single-copy (i.e., non-entangled) measurements fundamentally requires Ω(n3)Ω(n^3) copies, strictly exceeding the sample complexity Θ(n2)Θ(n^2) of learning classical Gaussian distributions. We show that this hardness persists even when few-copy entangled measurements are allowed. - Warm Gaussian states are classical to learn: When thermal fluctuations exceed the vacuum noise, parameterized by Σ(12+ν)IΣ\ge(\frac12+ν)I for any parameter ν>0ν>0, we prove that single-copy tomography requires N=Θ(n2min(n,1+ν1))N=Θ\left(n^2\min(n,1+ν^{-1})\right) copies. This bound is tight and is achieved by simple, non-adaptive, unentangled heterodyne measurements. Crucially, for ν=Ω(1)ν=Ω(1), the sample complexity drops to Θ(n2)Θ(n^2), matching the classical case. Our results tightly characterize a quantum-to-classical crossover in the learnability of bosonic Gaussian states, reveal a novel connection between fundamental physics and statistical learning theory, and have implications for real-world sensing experiments.


Source: arXiv:2609.26705v1 - http://arxiv.org/abs/2609.26705v1 PDF: https://arxiv.org/pdf/2609.26705v1 Original Link: http://arxiv.org/abs/2609.26705v1

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Date:
Sep 23, 2026
Topic:
Data Science
Area:
Machine Learning
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