The hardest problem in quantum computing isn’t the algorithm — it’s getting your data into the machine. Nikola from quantum startup Haikyuu AI explains why scalable data encoding, not hardware, is the real bottleneck, and how flexible middleware (and their open-source Rivet toolkit) turns failed quantum pilots into useful enterprise workflows.
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Why this episode matters: Most enterprise quantum pilots stall before touching real hardware. This is a grounded, hype-free look at what actually works today in finance, chemistry, and engineering — and how the quantum software stack will evolve toward an operating system.
In this episode, we cover:
- Why scalable data encoding — not model architecture — is quantum ML’s true bottleneck
- A noise-resilient encoding approach demonstrated on IBM hardware for anomaly detection
- Middleware stacks: combining compilation, error mitigation, and correction across vendors
- Rivet: Haikyuu AI’s open-source toolkit for chunked quantum workflows
- Who buys quantum middleware first: teams recovering from failed pilots
- The road to a “quantum operating system” — and the deep-tech founder mindset
Related episodes: Europe’s Scale-Up Gap Isn’t Capital — It’s Demand · Jan–Feb 2026 DACH capital review.
00:00 – A noise-resilient quantum encoding algorithm
07:19 – Why data encoding is the hard part
10:04 – Encoding across fluid dynamics, ML, and finance
14:54 – Building complex middleware stacks
19:44 – First customers: failed quantum pilots
24:02 – Where quantum helps today (and where it doesn’t)
28:39 – Toward a quantum operating system
30:57 – Lessons from past tech revolutions
36:17 – Seed stage, talent, and the long game
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