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  "slug": "error-attribution-as-a-resource-allocation-principle-for-quantum-error-correctio",
  "title": "Error Attribution as a Resource-Allocation Principle for Quantum Error Correction: Sensitivity-Guided Noise Reduction and Its Analytic Limits",
  "authors": [
    "Rowan Brad Quni-Gudzinas"
  ],
  "abstract": "Logical error rate is the standard benchmark for quantum error correction (QEC), but it is an aggregate quantity: it says nothing about which circuit components actually drive logical failure. Recent work introduced an error attribution scheme that computes per-component sensitivities ∂ P_L/∂ p_i of the logical error rate jointly from the same Monte Carlo samples used to estimate P_L, and showed that halving noise on the 5%–7% most sensitive components reduces logical error rates by roughly 15%–25%, about twice the improvement from randomly chosen components [1],[3]. This paper develops the quantitative structure underlying that result. We formalize the sensitivity map, derive the first-order intervention gain for targeted versus random noise-reduction budgets, and show analytically that the targeted-to-random advantage is ρ = r/(fr + 1 - f), where f is the hotspot fraction and r the sensitivity contrast; for $f = 0.06$ and $r = 2.136$ this ratio is exactly $2$, matching the reported factor. [...]",
  "abstract_markdown": "Logical error rate is the standard benchmark for quantum error correction (QEC), but it is an aggregate quantity: it says nothing about which circuit components actually drive logical failure. Recent work introduced an error attribution scheme that computes per-component sensitivities $\\partial P_L/\\partial p_i$ of the logical error rate jointly from the same Monte Carlo samples used to estimate $P_L$, and showed that halving noise on the 5%–7% most sensitive components reduces logical error rates by roughly 15%–25%, about twice the improvement from randomly chosen components [1],[3]. This paper develops the quantitative structure underlying that result. We formalize the sensitivity map, derive the first-order intervention gain for targeted versus random noise-reduction budgets, and show analytically that the targeted-to-random advantage is $\\rho = r/(fr + 1 - f)$, where $f$ is the hotspot fraction and $r$ the sensitivity contrast; for $f = 0.06$ and $r = 2.136$ this ratio is exactly $2$, matching the reported factor. [...]",
  "date": "2026-10-05",
  "first_published": "2026-10-05",
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  "url": "https://papers.qnfo.org/papers/error-attribution-as-a-resource-allocation-principle-for-quantum-error-correctio",
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        "name": "Rowan Brad Quni-Gudzinas",
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    "titles": [
      {
        "title": "Error Attribution as a Resource-Allocation Principle for Quantum Error Correction: Sensitivity-Guided Noise Reduction and Its Analytic Limits"
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        "description": "Logical error rate is the standard benchmark for quantum error correction (QEC), but it is an aggregate quantity: it says nothing about which circuit components actually drive logical failure. Recent work introduced an error attribution scheme that computes per-component sensitivities ∂ P_L/∂ p_i of the logical error rate jointly from the same Monte Carlo samples used to estimate P_L, and showed that halving noise on the 5%–7% most sensitive components reduces logical error rates by roughly 15%–25%, about twice the improvement from randomly chosen components [1],[3]. This paper develops the quantitative structure underlying that result. We formalize the sensitivity map, derive the first-order intervention gain for targeted versus random noise-reduction budgets, and show analytically that the targeted-to-random advantage is ρ = r/(fr + 1 - f), where f is the hotspot fraction and r the sensitivity contrast; for $f = 0.06$ and $r = 2.136$ this ratio is exactly $2$, matching the reported factor. [...]",
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