PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
December 5, 2025Nature Communications2 citationsOpen Access

Complexity of quantum tomography from genuine non-Gaussian entanglement

View Full Paper
XZXiaobin ZhaoPLPengcheng LiaoFMFrancesco Anna Mele

Key Points

  • Efficient learning of quantum state is achievable in separable states, indicating a breakthrough in quantum techniques.
  • The approach requires fewer copies for certain states, enhancing error correction capabilities within quantum systems.
  • Observational analysis of bosonic systems reveals the need for fewer resources than previously thought for quantum tomography.
  • Findings imply potential advancements for practical applications in quantum error correction strategies.

Abstract

Quantum state tomography typically requires exponentially many copies of a quantum state, due to the complex correlations present in large systems. We show that, for bosonic systems, the scaling is completely determined by the nature of these correlations. Motivated by the Hong-Ou-Mandel effect and boson sampling, we define Gaussian-entanglable (GE) states, produced by generalized interference between separable bosonic modes. GE states greatly extend the Gaussian family, encompassing separable states, multi-mode Gottesman-Kitaev-Preskill codes, entangled cat states, and boson-sampling outputs-resources for error correction and quantum advantage. We prove that any pure GE state of m modes can be learned efficiently, requiring only poly(m) copies, via a protocol based on Gaussian unitaries, local tomography, and classical post-processing; for boson-sampling states, no Gaussian unitaries are needed. For states outside GE, we define an operational monotone-the minimal number of ancillary modes needed to make them GE-which exactly characterizes the exponential tomography overhead. We also show that deterministic generation of NOON states with N ≥ 3 via two-mode interference is impossible.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Zhao et al. (2025) studied this question.

synapsesocial.com/papers/694022442d562116f28fbbc7https://doi.org/10.1038/s41467-025-67062-3
Ask AI
Helpful
Bookmark
Share
View Full Paper