Experimental runs on a superconducting quantum processor demonstrate that 20-qubit quantum neural networks are more resistant to adversarial attacks than classical networks, with adversarial training further improving robustness and empirical bounds closely matching theory.
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Recurrent quantum feature maps achieve lower mean squared error than echo state networks and multilayer perceptrons on Mackey-Glass prediction using compact quantum circuits.
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Experimental robustness benchmarking of quantum neural networks on a superconducting quantum processor
Experimental runs on a superconducting quantum processor demonstrate that 20-qubit quantum neural networks are more resistant to adversarial attacks than classical networks, with adversarial training further improving robustness and empirical bounds closely matching theory.
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Recurrent Quantum Feature Maps for Reservoir Computing
Recurrent quantum feature maps achieve lower mean squared error than echo state networks and multilayer perceptrons on Mackey-Glass prediction using compact quantum circuits.