From lab to marketplace: How do we succeed in quantum computing?
summary
Panelists lay out a concrete path: identify market-suitable problems, build proofs-of-concept, then scale — funded consortia (Planck, Sequoia, AutoQML, ProvideQ) are already moving companies from awareness to in-house R&D and startups. Key constraints are talent shortages, an 3-year learning curve, interdisciplinary teams and hardware–software co‑design, and uncertain timelines between competing qubit platforms. Current hardware limits (e.g. 27 qubits) force hybrid classical–quantum solutions; public funding and clustered local ecosystems plus targeted education are shown to reduce risk and accelerate commercialization.
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chapters (5)
- 0:00Start
- 0:26Johannes Schmidt
- 1:23Are There Special Challenges When It Comes to Quantum Computing When One Compares It to Other Technologies for Example Ai or Cloud Computing
- 7:28Gdpr Compliance
- 8:17Challenge in Integrating Quantum Computing
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