A hybrid quantum-classical surrogate compresses the latent time-stepping operator of flow models to as few as 8 trainable parameters and achieves stable long rollouts via exact unitarity, matching a classical baseline on turbulent and cardiovascular cases.
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Explainable quantum-compressed machine learning for complex fluid flows
A hybrid quantum-classical surrogate compresses the latent time-stepping operator of flow models to as few as 8 trainable parameters and achieves stable long rollouts via exact unitarity, matching a classical baseline on turbulent and cardiovascular cases.