This article discusses a paper that proposes a new approach to generating quantum circuits for amplitude encoding using transformer decoders. Quantum data encoding is a crucial step in noisy intermediate-scale quantum computers, which requires efficient circuit design. The proposed model exhibits high generalization ability with a small amount of training data and can generate appropriate quantum circuits even for unseen input data. Additionally, it was confirmed that the generated circuits have a shallower structure than the training data and are robust against noise. This research paves a new way toward automation and efficiency improvement in quantum computation.
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