Supervised ML trained on simulated gate set tomography data predicts noise models to build cross-hardware quantum emulators, validated by matching H2 unitary coupled cluster energy results to real hardware within 0.128% relative error.
2) where ⃗ζ represents the two-qubit depolarizing noise
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Designing a Machine Learning-Driven, Cross-Hardware Emulator for Noisy Quantum Computers with Gate-Based Protocols
Supervised ML trained on simulated gate set tomography data predicts noise models to build cross-hardware quantum emulators, validated by matching H2 unitary coupled cluster energy results to real hardware within 0.128% relative error.