Quantum error mitigation (QEM) aims to recover accurate physical quantities from noisy quantum devices without requiring full fault-tolerant error correction. Given a quantum state ρ affected by a noise channel 𝒩, the measured observable is
- noisy: ⟨O⟩ₙ = Tr[O 𝒩(ρ)],
- ideal: ⟨O⟩ᵢ = Tr[Oρ].
The goal of QEM is to design a mapping M such that:
M(⟨O⟩ₙ) ≈ ⟨O⟩ᵢ.
Existing approaches often use linear models, zero-noise extrapolation, or small neural networks. In this challenge, participants will build next-generation, data-driven QEM models capable of learning complex noise patterns and outperforming current ANN-based and classical QEM strategies.
