Entanglement improves classification accuracy in distributed quantum ML tasks across datasets, but excessive amounts degrade performance by reducing effective parameter dimension.
H.et al.Purification of noisy entanglement and faithful teleportation via noisy channels.Phys
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Noiseless linear amplification and attenuation improve average teleportation fidelity by up to 78% and increase the quantum advantage in superdense coding by more than 100% in some loss regimes, with optimal POVMs reducing to these operations.
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The power of entanglement in distributed quantum machine learning
Entanglement improves classification accuracy in distributed quantum ML tasks across datasets, but excessive amounts degrade performance by reducing effective parameter dimension.
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Utility of noiseless linear amplification and attenuation in single-rail discrete-variable quantum communications
Noiseless linear amplification and attenuation improve average teleportation fidelity by up to 78% and increase the quantum advantage in superdense coding by more than 100% in some loss regimes, with optimal POVMs reducing to these operations.