A black-box machine learning technique trains continuously-coupled photonic waveguide arrays to implement target unitaries using limited single- and two-photon measurements without requiring detailed internal models.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
fields
quant-ph 2years
2026 2verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative citing papers
Binary decision trees enable cost-effective multinomial classifiers from quantum binary models, matching other methods' accuracy with at most logarithmic overhead in the number of classes.
citing papers explorer
-
Training continuously-coupled reconfigurable photonic chips with quantum machine learning
A black-box machine learning technique trains continuously-coupled photonic waveguide arrays to implement target unitaries using limited single- and two-photon measurements without requiring detailed internal models.
-
Divide et impera: hybrid multinomial classifiers from quantum binary models
Binary decision trees enable cost-effective multinomial classifiers from quantum binary models, matching other methods' accuracy with at most logarithmic overhead in the number of classes.