Experimental demonstration of quantum anomaly detection on audio-encoded states using a three-qubit diamond spin processor, achieving a minimum error rate of 15.4%.
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DustNET is proposed as a shared dataset to train machine learning models that complement traditional physics equations for predictive modeling of dusty plasmas across laboratory and natural scales.
Numerical simulations on two and three qutrits show increased associative memory capacity when qubits are replaced by qutrits in a quantum annealing protocol.
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Quantum Anomaly Detection with a Spin Processor in Diamond
Experimental demonstration of quantum anomaly detection on audio-encoded states using a three-qubit diamond spin processor, achieving a minimum error rate of 15.4%.
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DustNET: enabling machine learning and AI models of dusty plasmas
DustNET is proposed as a shared dataset to train machine learning models that complement traditional physics equations for predictive modeling of dusty plasmas across laboratory and natural scales.
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Associative memory on qutrits by means of quantum annealing
Numerical simulations on two and three qutrits show increased associative memory capacity when qubits are replaced by qutrits in a quantum annealing protocol.