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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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.