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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.
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Continuous-variable designs and design-based shadow tomography from random lattices

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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

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