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.
Doriguello and Ashley Montanaro
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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.