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gaussian-exponential-zero-noise-extrapolation

Hybrid Gaussian-exponential zero-noise extrapolation methodology for periodic quantum circuits — combines Gaussian and exponential error models for more accurate expectation value estimation in NISQ-era quantum computing.

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Repository
hiyenwong/ai_collection
Letzte Quellaktivität
8. Juni 2026 um 08:11
Erkannte Sprache von SKILL.md
Englisch
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2
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0

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SKILL.md
Quellanweisungen · Schreibgeschützte Vorschau
name
gaussian-exponential-zero-noise-extrapolation
description
Hybrid Gaussian-exponential zero-noise extrapolation methodology for periodic quantum circuits — combines Gaussian and exponential error models for more accurate expectation value estimation in NISQ-era quantum computing.
category
quantum
# Hybrid Gaussian-Exponential Zero-Noise Extrapolation ## Context Based on arXiv:2605.29242 (Jun 2026). Proposes a hybrid Gaussian-exponential model for zero-noise extrapolation (ZNE) specifically designed for periodic quantum circuits, improving over standard exponential or polynomial extrapolation. ## Core Methodology 1. **Error model analysis**: Characterize error behavior in periodic quantum circuits — errors exhibit both Gaussian-like (short-depth) and exponential-like (long-depth) components 2. **Hybrid model construction**: Fit expectation values using a combined Gaussian-exponential model: f(λ) = A·exp(-αλ²) + B·exp(-βλ) + C, where λ is the noise scaling factor 3. **Zero-noise extrapolation**: Evaluate f(0) = A + B + C to estimate the noiseless expectation value 4. **Periodic circuit advantage**: The hybrid model captures the oscillatory behavior of errors in periodic circuits better than pure exponential or polynomial models 5. **Parameter estimation**: Use least-squares fitting on noisy expectation values at multiple noise scales ## Implementation Steps 1. Prepare the target quantum circuit 2. Apply noise amplification at multiple scale factors λ₁, λ₂, ..., λₙ (e.g., via gate folding or identity insertion) 3. Measure expectation values E(λᵢ) at each noise scale 4. Fit the hybrid Gaussian-exponential model to the data points 5. Extrapolate to λ = 0 to obtain the zero-noise estimate 6. Validate against known benchmarks or exact simulations ## Key Results - Hybrid model captures both Gaussian and exponential error components in periodic circuits - More accurate than standard exponential ZNE for circuits with oscillatory error behavior - Maintains polynomial sample complexity - Applicable to parameterized quantum circuits and variational algorithms ## Pitfalls - Requires fitting more parameters than standard exponential ZNE — needs more data points - Model may overfit if too few noise scales are used; use at least 5-7 scale factors - Not suitable for circuits with purely exponential or purely polynomial error behavior - Noise amplification must be accurate — gate folding errors propagate into the fit ## Verification - Compare hybrid model fit quality (R²) against pure exponential and polynomial models - Validate on circuits with known exact solutions - Test robustness: vary number of shots and noise scales to assess convergence - Verify that the extrapolated value improves circuit fidelity metrics ## Activation - zero noise extrapolation, ZNE, gaussian exponential model, periodic circuits, error mitigation, NISQ - 零噪声外推, 高斯指数模型, 周期量子线路, 误差缓解
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