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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The paper identifies four key hurdles in the transition from NISQ to FASQ quantum computers and argues that targeting them will accelerate progress toward useful quantum advantage.
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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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Mind the gaps: The fraught road to quantum advantage
The paper identifies four key hurdles in the transition from NISQ to FASQ quantum computers and argues that targeting them will accelerate progress toward useful quantum advantage.