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Research PaperResearchia:202610.06067

Finding Gaussian Structure in Bosonic States

Alvan Arulandu

Abstract

We study agnostic tomography of pure bosonic Gaussian states: given copies of an arbitrary $n$-mode bosonic state $ρ$, the goal is to output a pure Gaussian state whose infidelity with $ρ$ is at most $\mathrm{opt} + ε$, where $\mathrm{opt}$ is the minimum infidelity achievable by any pure Gaussian state. We give efficient protocols achieving this in both the high and low fidelity regimes. When $\mathrm{opt}$ is below some universal constant, our protocol has runtime and copy complexity which i...

Submitted: October 6, 2026Subjects: Machine Learning; Data Science

Description / Details

We study agnostic tomography of pure bosonic Gaussian states: given copies of an arbitrary nn-mode bosonic state ρρ, the goal is to output a pure Gaussian state whose infidelity with ρρ is at most opt+ε\mathrm{opt} + ε, where opt\mathrm{opt} is the minimum infidelity achievable by any pure Gaussian state. We give efficient protocols achieving this in both the high and low fidelity regimes. When opt\mathrm{opt} is below some universal constant, our protocol has runtime and copy complexity which is strongly polynomial in n,1/εn, 1/ε and log⁡log⁡E\log \log E, where EE is the energy of the closest pure Gaussian state. For arbitrary opt\mathrm{opt}, our protocol uses (n+1)poly(1/ε)poly(1+log⁡log⁡(E))(n+1)^{\mathrm{poly}(1/ε)} \mathrm{poly}\left(1+\log\log(E)\right) copies and runtime. As a corollary, we obtain the first truly tolerant Gaussianity testing protocol for distinguishing whether opt>c+ε\mathrm{opt} > c + ε or opt<c−ε\mathrm{opt} < c - ε, for any threshold c∈(0,1)c\in(0,1). We also prove poly(n,1/ε)\mathrm{poly}(n,1/ε) runtime is impossible, unless NP⊆BQP\mathrm{NP}\subseteq\mathrm{BQP}. Our protocols follow a shared paradigm: first, we iteratively use general Gaussian measurements combined with techniques from classical robust statistics to obtain a good warm start estimate, then we leverage non-Gaussian measurements to refine this warm start using convex and non-convex optimization methods. Interestingly, we prove that non-Gaussian measurements are necessary to match the strong agnostic guarantees we obtain, and in fact these guarantees are provably superior to what is possible for robustly estimating classical Gaussians.


Source: arXiv:2610.06810v1 - http://arxiv.org/abs/2610.06810v1 PDF: https://arxiv.org/pdf/2610.06810v1 Original Link: http://arxiv.org/abs/2610.06810v1

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Date:
Oct 6, 2026
Topic:
Data Science
Area:
Machine Learning
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