A Theory-Informed, Large Language Model-Assisted Competitive Intelligence Process for Assessing Competing Quantum Error-Correction Technologies

Emerging deep technologies create a competitive technical intelligence challenge because technical architectures, performance claims, ecosystem arrangements, and prospective markets evolve before stable adoption evidence is available. This article develops and illustrates a theory-informed, large language model (LLM)-assisted competitive intelligence (CI) process for assessing the disruptive potential of the development and commercialization of emerging quantum error-correction technologies. The process translates concepts from disruptive innovation, architectural innovation, value proposition analysis, and disruptive threat assessment into intelligence constructs, evidence questions, sequential prompt functions, source-validation criteria, and monitoring outputs. Perplexity.ai supported document discovery, organization, comparison, and preliminary synthesis; the authors evaluated sources, classified claims, and made all final interpretations. The process is illustrated through a comparative documentary analysis of Nord Quantique’s multimode bosonic quantum error-correction approach and IBM’s transmon, surface-code, and quantum low-density parity-check-oriented approach. The analysis identifies NQ as a documented architectural alternative that seeks to embed error protection within superconducting resonator modes. Its proposed reduction of error-correction overhead provides a plausible mechanism for changing system-resource requirements and deployment conditions. However, the retained evidence does not establish comparative system level advantage, lower total cost, a validated customer segment, a disruptive foothold, adoption, or incumbent displacement. The article contributes a source-disciplined and monitorable LLM-assisted CI process for emerging deep technologies. It enables analysts to distinguish documented observations, plausible mechanisms, and unverified outcomes; trace claim provenance; and identify the technical, market, ecosystem, and company signals that would justify revising an assessment. The process does not predict market winners or treat LLM output as empirical evidence.