The Problem-Substrate Mapping: A Framework for Honest Computational Investment
Year: 2026
Type: article
Abstract: Phase IV of the Qubit Delusion series. Translates the critique into a concrete investment portfolio: matching computational problem classes (optimization, linear algebra, probabilistic inference, quantum simulation, cryptography, general-purpose) to optimal physical substrates (Ising machines, optical processors, neuromorphic, analog quantum simulators, reversible classical, fault-tolerant QC). Proposes a standardized five-question evaluation framework with red flags and green flags. The resulting portfolio allocates ~45% to thermodynamic/analog, 25% to optical, 20% to neuromorphic, and ~10% to quantum — a near-inversion of current R&D allocation where gate-model QC absorbs ~60%. Full text and updates: https://papers.qnfo.org/papers/paper-problem-substrate-mapping/
Author: Quni-Gudzinas, Rowan Brad
Institution: Oldham Council
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7167699308
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