This framing around incentive misalignment really clarifies why most quantum startups chase qubit counts instead of solveable problems. QunaSys betting on chemestry applications specifically makes sense because molecular simulation has way lower error thresholds than most quantum algorithms. I remeber working with early ML frameworks where the tooling lagged hardware for years, and this feels similar.
Thanks for this. The incentive mismatch point is exactly what we were trying to surface, and your early-ML analogy is a good one.
Quick nuance on chemistry: it is a natural fit for quantum, but it is also very demanding, often needing high fidelity. That’s why so much of today’s work is still resource estimates and tightly scoped pilots, not production advantage yet.
This framing around incentive misalignment really clarifies why most quantum startups chase qubit counts instead of solveable problems. QunaSys betting on chemestry applications specifically makes sense because molecular simulation has way lower error thresholds than most quantum algorithms. I remeber working with early ML frameworks where the tooling lagged hardware for years, and this feels similar.
Thanks for this. The incentive mismatch point is exactly what we were trying to surface, and your early-ML analogy is a good one.
Quick nuance on chemistry: it is a natural fit for quantum, but it is also very demanding, often needing high fidelity. That’s why so much of today’s work is still resource estimates and tightly scoped pilots, not production advantage yet.
Appreciate you reading this.