A natural energy condition satisfied by most physical bosonic states, including outputs of universal bosonic circuits, allows the effective dimension for ε-approximations to scale as log(1/ε) instead of 1/ε², enabling improved learning and classical simulation algorithms.
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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.
A concise review of sample complexities and methods for tomography and learning in continuous-variable quantum systems, with emphasis on Gaussian versus non-Gaussian states.
citing papers explorer
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Exponentially-improved effective descriptions of physical bosonic systems
A natural energy condition satisfied by most physical bosonic states, including outputs of universal bosonic circuits, allows the effective dimension for ε-approximations to scale as log(1/ε) instead of 1/ε², enabling improved learning and classical simulation algorithms.
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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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Advances in quantum learning theory with bosonic systems
A concise review of sample complexities and methods for tomography and learning in continuous-variable quantum systems, with emphasis on Gaussian versus non-Gaussian states.