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Quantum circuits can approximate complex Sobolev functions on quantum state and operation spaces with provable, disentangled spectral and circuit approximation errors.
Chernoff-neural operators can achieve arbitrary precision in approximating complex semigroups, reshaping our approach to non-linear dynamics and stochastic control.
NeuralChaos achieves optimal approximation rates for square-integrable processes using significantly fewer evaluations than traditional methods, reshaping the landscape of stochastic modeling.
