A genetic algorithm over feature-to-qubit permutations improves quantum embedding fitness scores by small margins over random selection in simulated QML classifiers, but several comparisons omit negative results and lack error bars.
Invited: Trainable discrete feature embeddings for quantum machine learning
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
quant-ph 1years
2024 1verdicts
CONDITIONAL 1roles
background 1polarities
support 1representative citing papers
citing papers explorer
-
Optimizing Quantum Embedding using Genetic Algorithm for QML Applications
A genetic algorithm over feature-to-qubit permutations improves quantum embedding fitness scores by small margins over random selection in simulated QML classifiers, but several comparisons omit negative results and lack error bars.