{
  "id": "oai:papers.qnfo.org:epistemic-noise-as-computational-resource",
  "slug": "epistemic-noise-as-computational-resource",
  "title": "Epistemic Noise as Computational Resource",
  "authors": [
    "Rowan Brad Quni-Gudzinas"
  ],
  "abstract": "The scaling of quantum information processors is currently bottlenecked by the “isolationist” paradigm, which treats environmental decoherence as an irreversible entropy increase that must be suppressed. This manuscript proposes a radical engineering shift grounded in the Superdeterministic interpretation of quantum mechanics, where “random” measurement outcomes are viewed as epistemic limitations arising from a deterministic, computationally irreducible substrate. By modeling the environment not as a heat bath but as a correlated memory register, we demonstrate that what is conventionally discarded as noise contains recoverable information. We introduce a “Spectroscopic Qubit” architecture and a machine-learning-driven decoding protocol capable of extracting this hidden signal. Simulations of a superdeterministic toy model show a 92% prediction accuracy for “random” errors and a positive information backflow (N ≈ 0.232), validating the detection protocol. We define a new “Correlation Utility Index” (CUI) to standardize the measurement of recoverable non-Markovian resources and provide a detailed latency budget for real-time implementation. [...]",
  "abstract_markdown": "The scaling of quantum information processors is currently bottlenecked by the “isolationist” paradigm, which treats environmental decoherence as an irreversible entropy increase that must be suppressed. This manuscript proposes a radical engineering shift grounded in the Superdeterministic interpretation of quantum mechanics, where “random” measurement outcomes are viewed as epistemic limitations arising from a deterministic, computationally irreducible substrate. By modeling the environment not as a heat bath but as a correlated memory register, we demonstrate that what is conventionally discarded as noise contains recoverable information. We introduce a “Spectroscopic Qubit” architecture and a machine-learning-driven decoding protocol capable of extracting this hidden signal. Simulations of a superdeterministic toy model show a 92% prediction accuracy for “random” errors and a positive information backflow (\\(\\mathcal{N} \\approx 0.232\\)), validating the detection protocol. We define a new “Correlation Utility Index” (CUI) to standardize the measurement of recoverable non-Markovian resources and provide a detailed latency budget for real-time implementation. [...]",
  "date": "2026-01-01",
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