QNNs retain most hidden-task signals through public-task interfaces while classical networks transmit little, with transmission governed by teacher drift magnitude and the visible fraction of hidden drift in a unified geometric model.
Information-theoretic generalization bounds for learnin g from quantum data
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
quant-ph 2verdicts
UNVERDICTED 2representative citing papers
Extends Fano bounds to sufficiency of low conditional entropy and defines a quantum entanglement task for infinite-dimensional systems with bounds via maximal singlet fraction of finite-dimensional approximations.
citing papers explorer
-
Quantum Subliminal Learning
QNNs retain most hidden-task signals through public-task interfaces while classical networks transmit little, with transmission governed by teacher drift magnitude and the visible fraction of hidden drift in a unified geometric model.
-
On the coherent extension of some Fano-type learning bounds
Extends Fano bounds to sufficiency of low conditional entropy and defines a quantum entanglement task for infinite-dimensional systems with bounds via maximal singlet fraction of finite-dimensional approximations.