A parameterized quantum embedding can be trained to make a circuit output invariant under a finite group, which the authors interpret as learning an equivariant map between representations.
Cost-function embedding and dataset encoding for machine learning with parametrized quantum circuits
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
method 1
citation-polarity summary
fields
quant-ph 1years
2024 1verdicts
REJECT 1roles
method 1polarities
use method 1representative citing papers
citing papers explorer
-
Learning Equivariant Maps with Variational Quantum Circuits
A parameterized quantum embedding can be trained to make a circuit output invariant under a finite group, which the authors interpret as learning an equivariant map between representations.