yes

you are audible

1. You mentioned integrating quantum modules with existing discovery pipelines as a key challenge. Could you elaborate on what a 'hybrid' workflow might look like in practice for a mid-sized pharma company today?

2. Given the intense competition for quantum computing talent, what specific roles or skill sets should a pharmaceutical company prioritize hiring for right now to build a 'quantum-ready' team?

3. The ROI for early-stage quantum investment was listed as a hurdle. Beyond cost reduction, what other key performance indicators (KPIs) or value propositions should leaders use to build a business case for this technology?

4. The roadmap anticipates 100+ qubit systems in 3-5 years. What are the minimum hardware specifications (qubit count, error rates) needed to move beyond 'toy problems' and start generating actionable insights for a real drug candidate?

5. For a company just starting its quantum journey, which phase of the drug discovery pipeline, target identification, lead optimization, or clinical trial design, offers the most tangible 'quick win' to demonstrate value?

6. Quantum algorithms require specific data inputs. How much of a bottleneck is the need to clean, standardize, and prepare classical biological data (like genomic datasets) for use in these new quantum models?

7. For a company that decides to take a 'wait-and-see' approach until the technology matures further, what is the biggest risk they run? Is it falling behind in expertise, missing partnership opportunities, or something else?

8. You showed a slide on strategic quantum partnerships. From your research, what is a more effective model: partnering directly with a quantum hardware provider (like IBM, Microsoft) or with a specialized quantum software startup focused on life sciences?

9. If a drug is discovered or optimized using a quantum algorithm, how might that impact the regulatory approval process? Do you foresee agencies like the FDA needing new frameworks to validate these novel discovery methods?

10. How do you address the cultural resistance within traditional R&D teams who may be skeptical of results from a 'black box' quantum system they don't fully understand?

Questions collected via different channels

PPT is shared beforehand, kindly check here if you missed it! https://docs.google.com/presentation/d/1hwBfCVshGNoj4_XpZVnDNqFL3jOOEec3/edit?usp=drivesdk&ouid=106235845710924121068&rtpof=true&sd=true

1. For someone without a PhD in quantum physics, what is a realistic roadmap to enter this field? Are there specific software engineering or data science roles that support these teams?

2. Given that fault-tolerant quantum computers are still years away, what are the most valuable and practical skills to learn today to be prepared for the quantum computing era in pharma?

3. How do you see the collaboration working between a traditional software engineer and a quantum algorithm researcher on a actual project? What does that workflow look like?

4. Beyond the well-known examples of Google and IBM, what are some other companies or research labs where this work is being done that students should be aware of for opportunities?

One from my side - What advice would you give to future changemakers [students] from your domain expertise and apart from it, just for life.

Few tips for our club and it's members.