Banach-valued random feature models, including random single-hidden-layer networks, universally approximate elements of Bochner spaces over non-compact domains with explicit approximation rates.
Universal approximation theorem and error bounds for quantum neural networks and quantum reservoirs
2 Pith papers cite this work. Polarity classification is still indexing.
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A quantum Monte Carlo algorithm solves multidimensional Black-Scholes PDEs for option pricing with polynomial complexity in dimension d and accuracy 1/ε, with rigorous error bounds and a claimed speedup over classical Monte Carlo for bounded payoffs.
citing papers explorer
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Universal approximation property of Banach space-valued random feature models including random neural networks
Banach-valued random feature models, including random single-hidden-layer networks, universally approximate elements of Bochner spaces over non-compact domains with explicit approximation rates.
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Quantum Monte Carlo algorithm for option pricing and its complexity analysis
A quantum Monte Carlo algorithm solves multidimensional Black-Scholes PDEs for option pricing with polynomial complexity in dimension d and accuracy 1/ε, with rigorous error bounds and a claimed speedup over classical Monte Carlo for bounded payoffs.