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.
Design space exploration of hybrid quantum–classical neural networks,
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Simulations show hybrid quantum neural networks on Iris data degrade under depolarizing and amplitude-damping noise while phase-flip and phase-damping noise are less damaging, with ZNE, DDD, LRE, and PEC providing limited mitigation that depends on noise type and strength.
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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Robustness Evaluation of Hybrid Quantum Neural Networks under Noise Models via System-Level Error Mitigation
Simulations show hybrid quantum neural networks on Iris data degrade under depolarizing and amplitude-damping noise while phase-flip and phase-damping noise are less damaging, with ZNE, DDD, LRE, and PEC providing limited mitigation that depends on noise type and strength.