Two machine learning models reconstruct continuous Wigner functions from sparse phase-space measurements: a provably efficient regression model for sparse states (O(s⁴ log d) samples) and a self-supervised neural network for general states including experimental GKP code data.
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Sparse phase ansatzes for the SNAP-displacement protocol achieve favorable fidelity versus resource trade-offs for qudit state preparation up to dimension 64 in both ideal and noisy regimes.
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Learning to Reconstruct Wigner Functions in Phase Space
Two machine learning models reconstruct continuous Wigner functions from sparse phase-space measurements: a provably efficient regression model for sparse states (O(s⁴ log d) samples) and a self-supervised neural network for general states including experimental GKP code data.
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Sparse Phase Ansatzes for Resource-Efficient Qudit State Preparation via the SNAP-Displacement Protocol
Sparse phase ansatzes for the SNAP-displacement protocol achieve favorable fidelity versus resource trade-offs for qudit state preparation up to dimension 64 in both ideal and noisy regimes.