Mapping the Nordic Quantum Ecosystem

Nordic Innovation is looking for a Supplier to Map the Nordic Quantum Ecosystem the Nordic-Baltic quantum ecosystem – country by country with focus on an aggregated Nordic-Baltic level analysis and produce Nordic and Baltic use cases in quantum technology to illustrate the application of the technology in Nordic and Baltic businesses.

This in my view is a fantastic opportunity to use AI!

Automating the aggregation and synthesis of existing mappings, reports, and data for the Nordic-Baltic quantum technology ecosystem involves leveraging advanced capabilities of an LLM (Large Language Model) alongside complementary tools and methodologies. Here’s an elaborate breakdown of how this could be accomplished:

1. Aggregation of Data Sources

Data Identification:

  • Online Sources: Search and retrieve reports, papers, government publications, and articles related to the quantum ecosystem from Nordic and Baltic government websites, research institutions, and innovation agencies.

  • Databases: Mine relevant academic databases (e.g., PubMed, IEEE Xplore, Google Scholar) and repositories for research on quantum technology.

  • Existing Mappings: Extract information from previously completed national mappings, like those by VINNOVA, Danish Quantum Community, or NATO’s DIANA program.

  • Corporate Data: Collect publicly available information about businesses in the ecosystem (e.g., websites, press releases, and LinkedIn profiles).

Automation Tools:

  • Use web scraping tools to fetch and compile data from diverse sources.

  • APIs for scholarly databases or governmental repositories to automate data extraction.

  • LLMs to extract and summarize key points from large text datasets.

2. Data Structuring

Taxonomy Development:

  • Create a standardized taxonomy for organizing the data (e.g., categorizing stakeholders as research institutions, startups, government initiatives, etc.).

  • Develop structured fields such as country, type of actor, role in the ecosystem, technologies used, applications, and funding sources.

Data Parsing:

  • Use NLP techniques to parse unstructured text data into structured formats, such as tables or databases.

  • Extract metadata like publication dates, authors, and sources to ensure traceability.

3. Analysis and Synthesis

Keyword Extraction:

  • Identify key themes, trends, and technologies (e.g., quantum computing, sensors, encryption) using techniques like topic modeling or named entity recognition.

  • Extract insights related to regional strengths, such as the number of active startups, funding initiatives, or academic programs.

Cross-Country Comparison:

  • Analyze similarities and differences between national initiatives, identifying potential synergies or gaps in coverage.

  • Use quantitative analysis (e.g., frequency of specific keywords) and qualitative insights to assess comparative strengths.

Synthesizing Findings:

  • Summarize data into actionable insights, such as key ecosystem drivers, challenges, and opportunities.

  • Use LLMs to draft narrative reports highlighting strengths, weaknesses, and areas for collaboration.

4. Visualization and Reporting

Visual Mapping:

  • Generate visual tools like heatmaps, network diagrams, and infographics to depict relationships between stakeholders, geographic distribution, and areas of focus.

  • Use platforms like Tableau or Power BI for dynamic visualizations.

Highlight Nordic Strongholds:

  • Visualize complementary areas where Nordic and Baltic countries excel, such as collaborative clusters or centers of excellence in quantum computing.

Deliverables:

  • Create ready-to-use content for reports, presentations, and workshops, aligning with the Nordic Design Manual.

5. Ensuring Data Quality

Validation and Cross-Referencing:

  • Cross-check data from multiple sources to verify accuracy.

  • Use peer reviews or expert input to ensure that synthesized findings are comprehensive and valid.

Version Control:

  • Maintain a version-controlled repository of data, ensuring updates are documented and traceable.

6. Continuous Updates

Dynamic Reporting:

  • Develop a system for continuously aggregating and synthesizing data as new reports or initiatives emerge.

  • Use automated alerts for new publications or updates from key sources.

Tools and Technologies for Automation

  • LLMs: For summarizing reports, extracting insights, and drafting narratives.

  • Web Scrapers: Tools like BeautifulSoup or Selenium for automated data collection.

  • APIs: Accessing scholarly databases and government data repositories.

  • Data Cleaning Tools: OpenRefine for preprocessing raw data.

  • Visualization Tools: Tableau, Power BI, or custom scripts in Python (e.g., Matplotlib, Plotly).

Quantum Computing, Startup & Venture Capital, Startup EcosystemFrancesca Tabor