Pi-QEM selects dominant low-weight Pauli strings for ML training in quantum error mitigation, reducing ground-state energy estimation error by up to 34.01% using a single observable in molecular simulations on noisy IBM backends.
Error mitigation with clifford quantum-circuit data
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
fields
quant-ph 3years
2026 3representative citing papers
Implicit ZNE parameter choices flip significance in ~12% of tested configurations and hardware drift changes apparent effect size up to 3.4×, so QEM benchmarks need stricter statistical reporting.
A residual neural network trained on one quantum device's noise data can be fine-tuned with 20 samples from a second device to improve prediction of ideal circuit outputs, recovering 34.9% of the performance gap.
citing papers explorer
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Pauli Weight Hamiltonian Term Selection for Optimized Machine Learning Based Quantum Error Mitigation
Pi-QEM selects dominant low-weight Pauli strings for ML training in quantum error mitigation, reducing ground-state energy estimation error by up to 34.01% using a single observable in molecular simulations on noisy IBM backends.
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Claim against Measurement: Statistical Artefacts in Quantum Error Mitigation Benchmarks
Implicit ZNE parameter choices flip significance in ~12% of tested configurations and hardware drift changes apparent effect size up to 3.4×, so QEM benchmarks need stricter statistical reporting.
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Few-Shot Cross-Device Transfer for Quantum Noise Modeling on Real Hardware
A residual neural network trained on one quantum device's noise data can be fine-tuned with 20 samples from a second device to improve prediction of ideal circuit outputs, recovering 34.9% of the performance gap.