Coherent-state propagation enables quasi-polynomial classical simulation of bosonic circuits with logarithmically many Kerr gates at exponentially small trace-distance error, with polynomial runtime in the weak-nonlinearity regime.
Neural-network quantum state tomography
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
fields
quant-ph 3verdicts
UNVERDICTED 3representative citing papers
A thresholding bandit algorithm on data from a single-parameter entanglement-witness family enables conclusive batch entanglement detection for two-qubit states in class F, with MAB-derived sample-complexity bounds.
Machine learning classifies six Markovian and non-Markovian noise classes in two-qubit systems with over 94% accuracy using only final transfer efficiencies from a coherent population transfer protocol under three driving conditions.
citing papers explorer
-
Coherent-State Propagation: A Computational Framework for Simulating Bosonic Quantum Systems
Coherent-state propagation enables quasi-polynomial classical simulation of bosonic circuits with logarithmically many Kerr gates at exponentially small trace-distance error, with polynomial runtime in the weak-nonlinearity regime.
-
Batch Entanglement Detection in Parameterized Qubit States using Classical Bandit Algorithms
A thresholding bandit algorithm on data from a single-parameter entanglement-witness family enables conclusive batch entanglement detection for two-qubit states in class F, with MAB-derived sample-complexity bounds.
-
Detection of noise correlations in two qubit systems by Machine Learning
Machine learning classifies six Markovian and non-Markovian noise classes in two-qubit systems with over 94% accuracy using only final transfer efficiencies from a coherent population transfer protocol under three driving conditions.