Chasing shadows with Gottesman-Kitaev-Preskill codes

The infinitude of the continuous variable (CV) phase space is a serious obstacle in designing randomized tomography schemes with provable performance guarantees. A typical strategy to circumvent this issue is to impose a regularization, such as a photon-number cutoff, to enable the definition of ensembles of random unitaries on effective subspaces. In this work, we consider the task of performing shadow tomography of a logical subsystem defined via the Gottesman-Kitaev-Preskill (GKP) error correcting code. In particular, we construct a logical shadow tomography protocol via twirling of CV-POVMs by displacement operators and Gaussian unitaries. In the special case of heterodyne measurement, the shadow tomography protocol yields a probabilistic decomposition of any input state into Gaussian states that simulate the encoded logical information of the input relative to a fixed GKP code and we prove bounds on the Gaussian compressibility of states in this setting. For photon-parity measurements, logical GKP shadow tomography is equivalent to a Wigner sampling protocol for which we develop the appropriate sampling schemes and finally, using the existence of a Haar measure over symplectic lattices, we derive a Wigner sampling scheme via random GKP codes. This protocol establishes, via explicit sample complexity bounds, how Wigner samples of any input state from random points relative to a random GKP codes can be used to estimate any sufficiently bounded observable on CV space.