Provides a quantitative universal approximation theorem with error bounds for noisy quantum neural networks and tests on real hardware for quantitative finance.
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TDVP-MPS simulations of Rydberg atom chains mitigate exponential concentration in QELM, yielding competitive MNIST accuracy via controlled entanglement and disorder without requiring exact quantum dynamics.
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Quantitative Universal Approximation for Noisy Quantum Neural Networks
Provides a quantitative universal approximation theorem with error bounds for noisy quantum neural networks and tests on real hardware for quantitative finance.
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Harnessing Quantum Dynamics for Robust and Scalable Quantum Extreme Learning Machines
TDVP-MPS simulations of Rydberg atom chains mitigate exponential concentration in QELM, yielding competitive MNIST accuracy via controlled entanglement and disorder without requiring exact quantum dynamics.