Complex-valued and quantum-circuit word embeddings trained with a fidelity-based Skip-gram loss match classical word2vec on similarity benchmarks, provided the circuits are fit to the complex embeddings rather than trained directly.
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Learning Complex Word Embeddings in Classical and Quantum Spaces
Complex-valued and quantum-circuit word embeddings trained with a fidelity-based Skip-gram loss match classical word2vec on similarity benchmarks, provided the circuits are fit to the complex embeddings rather than trained directly.