What resources are needed for optimal learning of bosonic Gaussian states?
Continuous-variable systems enable key quantum technologies in computation, communication, and sensing. Bosonic Gaussian states emerge naturally in various such applications, including gravitational-wave and dark-matter detection. A fundamental question is how to characterize an unknown bosonic Gaussian state from as few samples as possible. Despite decades-long exploration, the ultimate efficiency limit remains unclear. In this work, we study the necessary and sufficient number of copies to learn an $n$-mode Gaussian state, with energy less than $E$, to $\varepsilon$ trace distance with high probability. We prove a lower bound of $Ω(n^3/\varepsilon^2)$ for Gaussian measurements, matching...
| Authors | Senrui Chen, Francesco Anna Mele, Marco Fanizza et al. |
|---|---|
| Primary category | quant-ph |
| Published | 18 March 2026 |
| Last updated at source | 13 September 2026 |
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