Qubit-count growth is your scenario assumption.
The simulator compounds the annual rate you choose, rounds to whole qubits, and keeps that assumption visible. It is not a forecast of IonQ hardware or industry performance.
q(t)
Put double-exponential progress on a strategy timeline.
Separate the assumption from the math. Set a qubit-growth path, compare it with another, and inspect how every added qubit doubles the modeled state space.
Modeled outcome
Primary path extends the modeled state-space lead.
68primary qubits
20.47log10 states
PrimaryComparison
State space shown as log10(2q)Two growth mechanisms. One strategy question.
Each added qubit doubles modeled state space.
For q qubits, the basis state space is 2q. The chart uses q × log10(2) so very large values remain readable.
The compounding is the strategic signal.
When q itself grows exponentially, the modeled state-space count 2q rises double-exponentially with time. Compare assumptions before setting milestones.
Move from curiosity to posture.
At year 3, the primary path reaches 68 qubits versus 39. The modeled state-space gap is 8.73 orders of magnitude.
Track assumptions, not slogans.
Put the qubit-growth rate, timeframe, and rounding rule beside every strategy claim. The export keeps those inputs attached to the output.
01Choose trigger points.
Use scenario gaps to ask when talent, partnerships, experimentation, or security migration deserve budget. The model frames the timing question; it does not answer it with invented live data.
02Revisit the path.
A strategy model is useful when assumptions can change. Save a scenario in the browser, reset it cleanly, and export a new series when the planning context moves.
03Inspect every year.
Exact modeled values from your selected assumptions, with state space represented logarithmically to avoid fake precision or unreadable integers.
| Year | Primary qubits | Primary log10 states | Comparison qubits | Comparison log10 states |
|---|
What does “double exponential” mean here?
If the number of qubits q(t) grows exponentially with time and modeled basis states equal 2q(t), then the state-space count is an exponential applied to an exponential. That mathematical relationship does not by itself establish useful computation, fidelity, error correction, or commercial performance.
Why use logarithmic state space?
The values become enormous quickly. Showing log10(2q) preserves the growth relationship and makes scenario differences readable without printing misleading walls of digits.
Carry the assumptions into the room.
Export the complete modeled series, including both annual growth assumptions and the exact formula used.