Quantum computing for market research
Bibliographic record
Abstract
The digital ecosystem continues to expand around the world and is revolutionising the way markets are researched. Indeed, consumer experiences are advertised and disseminated through so many channels and media that it has become a major challenge for researchers and marketing practitioners to collect, process and generate valuable information to support strategic and operational decisions. In this article, the authors explore how advances in quantum computing, which can be used to process huge amounts of data quickly and accurately, could offer an unprecedented opportunity for researchers to address the challenges of the digital ecosystem. Three studies are presented to define the state of the art and future expectations of quantum computing in market research and business. By means of a bibliometric analysis of 209 publications and a content analysis of the 30 highest-impact articles, we describe the present landscape, and also forecast the future with the help of in-depth interviews with eight experts. The findings reveal that the US and China are at the forefront of scientific development, but the contributions from four other countries (India, the UK, Canada and Spain) are also in double figures. However, graphical analysis identifies four poles of development: the US orbit, which includes Canada and Spain; the Chinese orbit, which includes India; the UK orbit; and the Australian orbit. In terms of expectations, the experts agree on the opportunities offered by quantum computing, but there is less consensus as to how long it will take to develop.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.012 |
| Scholarly communication | 0.010 | 0.020 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".