🤖 AI Summary
Efficient characterization of Gottesman–Kitaev–Preskill (GKP) states in continuous-variable (CV) quantum systems remains challenging due to the lack of scalable, physically implementable shadow tomography frameworks.
Method: We propose the first experimentally feasible CV shadow tomography framework based on rigged 2-designs. Leveraging the lattice structure of GKP states, we rigorously prove that the full set of GKP states forms a CV rigged 2-design and construct continuous-variable state designs accordingly. We then develop both global and local GKP shadow protocols.
Contribution/Results: We derive optimal sample complexity bounds for GKP state estimation and provide a complete optical implementation blueprint—including phase-space sampling, coherent detection, and classical post-processing. This work overcomes a key bottleneck in extending discrete-variable shadow tomography to the CV regime and establishes a scalable, low-overhead experimental pathway for real-time state verification in fault-tolerant CV quantum error correction.
📝 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.