Two hardware curves drive the whole model. Logical qubit count follows a doubling law and the physical two-qubit error rate decays exponentially:
Q(t) = Q0 · 2^(t / D) D = doubling period (yr)
ε(t) = ε0 · e^(−k·t) k = error-suppression rate (1/yr)
Each application segment only becomes economically viable — reaches "quantum advantage" — once both curves cross its own hardware bar (qubit count high enough and error rate low enough for the required circuit depth). Before that it earns nothing; a locked segment is not "growing slowly", it is exactly zero.
The instant a segment unlocks, its revenue follows the Bass diffusion model — the same equation used to forecast adoption of any new technology, driven by an innovation term p (early adopters) and an imitation term q (word of mouth / network effects):
dN/dt = (p + q·N/m) · (m − N) N = cumulative adopters
R(t) = N(t) · value-per-adopter R = segment revenue
The investment sentiment slider scales p and q together — more capital chasing a newly-unlocked segment means faster diffusion, not a higher ceiling m. The three towers you see are Bass diffusion curves rendered as growing revenue columns, gated by the qubit/error thresholds shown per segment; the rising particle stream visualises the same cumulative revenue as flowing capital.
- Qubit doubling period — shorter means the hardware frontier races ahead faster (matches industry roadmaps of 1.5–3 years).
- Error suppression — how fast logical error rates fall as error-correction codes mature; higher unlocks precision-sensitive segments (cryptanalysis) sooner.
- Investment sentiment — market appetite once a segment is technically unlocked; does not change *when* it unlocks, only how fast revenue ramps after.