Epistemic Noise as Computational Resource: A Superdeterministic Approach to Quantum Signal Processing

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. This work reframes the path to fault tolerance from a battle against ontology to a challenge of decryption.