Generative optimization of quantum embedding circuits improves supervised classification on some datasets, with derived bounds showing performance saturation governed by Wasserstein distance of the classical input data.
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2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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quant-ph 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
DDQN reinforcement learning automates VITE circuit design, producing circuits with ~37% fewer gates and ~43% less depth than hardware-efficient ansatze for Max-Cut while reaching Full-CI for H2 with shallower depth.
citing papers explorer
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Generative Quantum Data Embeddings for Supervised Learning
Generative optimization of quantum embedding circuits improves supervised classification on some datasets, with derived bounds showing performance saturation governed by Wasserstein distance of the classical input data.
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Investigation of Automated Design of Quantum Circuits for Imaginary Time Evolution Methods Using Deep Reinforcement Learning
DDQN reinforcement learning automates VITE circuit design, producing circuits with ~37% fewer gates and ~43% less depth than hardware-efficient ansatze for Max-Cut while reaching Full-CI for H2 with shallower depth.