Quantum Fourier generative models are trained classically at over 1000-qubit scale using log-likelihood loss from Parseval's identity and deployed on superconducting hardware for fast sampling that preserves multi-modal structure.
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quant-ph 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Qudit extension of parameterized IQP circuits proposed for generative modeling of integer data, with loss function and covariance matrix, validated on electron shower energy deposits in CLIC electromagnetic calorimeter.
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Quantum Fourier Generative Models Trainable at Large Scale
Quantum Fourier generative models are trained classically at over 1000-qubit scale using log-likelihood loss from Parseval's identity and deployed on superconducting hardware for fast sampling that preserves multi-modal structure.
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Qudit extension of parameterized IQP circuits: A generative quantum machine learning approach to integer data
Qudit extension of parameterized IQP circuits proposed for generative modeling of integer data, with loss function and covariance matrix, validated on electron shower energy deposits in CLIC electromagnetic calorimeter.