Canadian Quantum Ecosystem: Lessons From the 5th Workshop on Quantum Computing Entrepreneurship
Bibliographic record
Abstract
This article is part of a series exploring the unique characteristics and challenges of managing technology teams in emerging fields, with a particular focus on quantum computing [1-5]. The series delves into the experiences of entrepreneurs, researchers, and organizations at the forefront of the quantum revolution, offering insights into team management, innovation, and ecosystem development. Canada's vibrant quantum computing ecosystem is undergoing rapid expansion driven by a combination of government support, world-class academic institutions, and a thriving entrepreneurial community. The 5th Workshop on Quantum Computing Entrepreneurship, held during IEEE Quantum Week 2024, was organized by volunteers from the IEEE Technology & Engineering Management Society (TEMS), IEEE Entrepreneurship, and the Institut quantique. This workshop explored the key pillars of Canada's quantum ecosystem, showcasing the experiences of those driving the quantum revolution. These pillars encompass national and regional organizations that foster ecosystem growth, quantum accelerators that facilitate technology transfer from academia to industry, and the startups driving commercialization. Beyond offering valuable insights into the Canadian quantum ecosystem, this article also highlights fundamental components essential to any quantum ecosystem, given the current maturity of the technology. Readers involved in policy, entrepreneurship, or technology management can apply these lessons to foster quantum ecosystems in their own regions or organizations.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".