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.
Serafini,Quantum continuous variables: a primer of theoretical methods(CRC press, 2017)
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Modulating relative weights of interaction channels in a quantum Brownian motion model allows control over non-Markovianity, inducing transitions to Markovian regimes using Gaussian master equations.
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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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Controlling the non-Markovianity of quantum Brownian motion
Modulating relative weights of interaction channels in a quantum Brownian motion model allows control over non-Markovianity, inducing transitions to Markovian regimes using Gaussian master equations.