Selecting a customized Hermitian observable enables training of QNNs up to 10 qubits under noise for global cost functions, outperforming Pauli observables, while PauliZ works best for local cost functions up to 10 qubits.
Variational quantum algorithms
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
quant-ph 2verdicts
UNVERDICTED 2representative citing papers
Magnitude-only encoding reaches 99.57% accuracy on 3-class and 71.19% on 8-class SAR tasks in hybrid models, beating phase-inclusive alternatives, while phase boosts pure quantum models by up to 21.65 points.
citing papers explorer
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HQNET: Harnessing Quantum Noise for Effective Training of Quantum Neural Networks in NISQ Era
Selecting a customized Hermitian observable enables training of QNNs up to 10 qubits under noise for global cost functions, outperforming Pauli observables, while PauliZ works best for local cost functions up to 10 qubits.
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Magnitude Is All You Need? Rethinking Phase in Quantum Encoding of Complex SAR Data
Magnitude-only encoding reaches 99.57% accuracy on 3-class and 71.19% on 8-class SAR tasks in hybrid models, beating phase-inclusive alternatives, while phase boosts pure quantum models by up to 21.65 points.