aiqbench analysis
This paper advances continuous-variable quantum computing by demonstrating how lattice-like GKP states can form rigged state 2-designs, enabling efficient shadow tomography protocols with derived sample complexity bounds. By providing the necessary physical gadgets for implementation, the work bridges theoretical design constraints with practical experimental requirements for large-scale quantum systems.
Continuous-variable designs and design-based shadow tomography from random lattices
arXiv:2412.17909v4 Announce Type: replace Abstract: We investigate state designs for continuous-variable quantum systems using the aid of lattice-like quantum states. These are code states of Gottesman-Kitaev-Preskill (GKP) codes. We show that for an n-mode system, the set of all GKP states forms a rigged continuous-variable state 2-design. We use these lattice state designs to construct a continuous variable shadow tomography protocol, derive sample complexity bounds for both global- and local GKP shadows under reasonable physical assumptions, and provide the physical gadgets needed to implement this protocol.
Abstract:We investigate state designs for continuous-variable quantum systems using the aid of lattice-like quantum states. These are code states of Gottesman-Kitaev-Preskill (GKP) codes. We show that for an n-mode system, the set of all GKP states forms a rigged continuous-variable state 2-design. We use these lattice state designs to construct a continuous variable shadow tomography protocol, derive sample complexity bounds for both global- and local GKP shadows under reasonable physical assumptions, and provide the physical gadgets needed to implement this protocol.
5+31 pages, 3 figures, comments welcome! v2-v4 contain corrections of minor errors and further clarifications
Quantum Physics (quant-ph); Information Theory (cs.IT); Operator Algebras (math.OA)
arXiv:2412.17909 [quant-ph]
(or arXiv:2412.17909v4 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2412.17909 Focus to learn more arXiv-issued DOI via DataCite
https://doi.org/10.1103/dy4m-gq5c 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 · 204 words
Related coverage

Neural nets design quantum gates with 99.95% accuracy
Quantum Zeitgeist · Oct 1, 2026

How to protect against OAuth-based supply chain breaches and credential sprawl
1Password · Apr 23, 2026

Frontier AI Risk Report (February–March 2026)
METR · May 19, 2026

Informe de riesgos de la IA de frontera (febrero–marzo de 2026)
METR · May 19, 2026