Skip to content
aiqbench takeQuantum Computing

aiqbench analysis

This research establishes that simultaneously estimating multiple nonlinear functionals of a quantum state requires only a sample complexity linear in the number of terms, effectively reducing the difficulty to that of single-functional estimation. The findings directly advance practical protocols for entanglement spectroscopy and virtual cooling within quantum many-body systems by optimizing resource usage.
Sources coveringarXiv, Quantum Zeitgeist
What this isA model's reading of the article and related coverage, written for aiqbench. Not the publisher's words.

Simultaneous Estimation of Nonlinear Functionals of a Quantum State

arXiv1 min read

arXiv:2505.16715v2 Announce Type: replace Abstract: We consider a fundamental task in quantum information theory, estimating the values of $tr(O\rho)$, $tr(O\rho^2)$, $\ldots$, $tr(O\rho^k)$ for an observable $O$ and a quantum state $\rho$. We show that $\widetilde\Theta(k)$ samples of $\rho$ are sufficient and necessary to simultaneously estimate all the $k$ values. This means that estimating all the $k$ values is almost as easy as estimating only one of them, $tr(O\rho^k)$. As an application, our approach advances the sample complexity of entanglement spectroscopy and the virtual cooling for quantum many-body systems. Moreover, we extend our approach to estimating general functionals by polynomial approximation.

Abstract:We consider a fundamental task in quantum information theory, estimating the values of $tr(O\rho)$, $tr(O\rho^2)$, $\ldots$, $tr(O\rho^k)$ for an observable $O$ and a quantum state $\rho$. We show that $\widetilde\Theta(k)$ samples of $\rho$ are sufficient and necessary to simultaneously estimate all the $k$ values. This means that estimating all the $k$ values is almost as easy as estimating only one of them, $tr(O\rho^k)$. As an application, our approach advances the sample complexity of entanglement spectroscopy and the virtual cooling for quantum many-body systems. Moreover, we extend our approach to estimating general functionals by polynomial approximation.

28 pages. v2: minor revision

arXiv:2505.16715 [quant-ph]

(or arXiv:2505.16715v2 [quant-ph] for this version)

https://doi.org/10.48550/arXiv.2505.16715 Focus to learn more arXiv-issued DOI via DataCite

https://doi.org/10.1109/TIT.2026.3699531 Focus to learn more DOI(s) linking to related resources

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Full text under the publisher's open licence · 199 words

Related coverage