About track
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.
Timeline
Start
Dec 2025 Mon, GMT
End
Jan 2026 Mon, GMT
Why this track exists?
Participants will construct a full QEM pipeline based on three main components: data generation, model design, and benchmarking. The aim is to develop a mapping
fθ: xₙ → x̂ᵢ
where xₙ represents noisy measurement statistics (bitstring probabilities, expectation vectors, or raw counts) and x̂ᵢ is the model’s estimate of the ideal value.
Who is this track for?
No audience yet.
Prizes & Outcomes
No prizes yet.
How to get started
Participants will generate paired data {(xₙᵏ, xᵢᵏ)}ₖ from quantum circuits U with varying depths, architectures, and noise strengths. For a circuit U(θ) preparing ρ = U|0…0⟩⟨0…0|U†:
- compute ideal measurement statistics
- pᵢ(b) = |⟨b|ρ|b⟩|², or
- ideal expectation values ⟨O⟩ᵢ = Tr[Oρ],
- inject noise channels 𝒩 such as
- depolarizing: 𝒩_dep(ρ) = (1 − p)ρ + p I / 2ⁿ,
- amplitude damping,
- readout confusion/noise,
- sample noisy observables xₙ through repeated measurements.