PIDN replaces repeated multi-noise ZNE evaluations with a trained network that denoises expectation values and gradients from noisy data plus history, achieving comparable optimization on quantum models with 4-6x fewer circuits.
Classical benchmarking of zero noise extrapolation beyond the exactly-verifiable regime
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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quant-ph 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Ground-state energies of homogeneous and random-coupling Ising models are obtained via CVQE with GSA on quantum hardware up to 63 qubits, with error-boundary, entropic, and subspace analyses indicating suitability for near-term devices.
citing papers explorer
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Accelerating Noisy Variational Quantum Algorithms with Physics-Informed Denoising Networks
PIDN replaces repeated multi-noise ZNE evaluations with a trained network that denoises expectation values and gradients from noisy data plus history, achieving comparable optimization on quantum models with 4-6x fewer circuits.
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Ground-state energies of Ising models calculated using the samples from a quantum computer that simulates short-time evolution
Ground-state energies of homogeneous and random-coupling Ising models are obtained via CVQE with GSA on quantum hardware up to 63 qubits, with error-boundary, entropic, and subspace analyses indicating suitability for near-term devices.