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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2026 2verdicts
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A non-unitary extension of Grover's algorithm achieves O(sqrt(N)) query complexity matching the optimal bound by using a single large rotation via block encoding and Chebyshev approximation, at the cost of one additional qubit.
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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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Non-unitary extension of Grover's search algorithm
A non-unitary extension of Grover's algorithm achieves O(sqrt(N)) query complexity matching the optimal bound by using a single large rotation via block encoding and Chebyshev approximation, at the cost of one additional qubit.