A survey analysis of quantum computing adoption and the paradigm of privacy engineering
Bibliographic record
Abstract
Abstract This study investigates the adoption of quantum computing (QC) technology using the diffusion of innovation (DOI) theory and provides an extensive literature review. We deployed structural equation modeling to analyze data from a survey conducted among 96 top managers in various industries from Canada, the US, and Europe, including IT‐based small and medium‐sized enterprises (SMEs) dealing with QC software development. Our survey analysis indicates that the complexity of QC systems and software is the main barrier to the future adoption of quantum computing. This research offers insights into how future quantum computers can impact the security and privacy of information, emphasizing the importance of privacy protection. In this context, the study contributes to the notion of privacy engineering in the complex context of QC. The study established important outlines and tools for shaping future QCs. Our study, backed by empirical evidence, underscores the significant impact of new technology on citizens', organizations', firms', and government‐private data. The results provide a clear message to policymakers, industry leaders, and developers: privacy engineering should be an integral part of technical development, and it's crucial to act before costs escalate. In this context, our study stands out as one of the few that use NLP and structural equation modeling to address privacy challenges in QC research through experimental research, offering practical solutions to real‐world problems.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".