Use cases · Mobility · aerospace

A dated roadmap for aerodynamic flow simulation

A flow-transport solver mapped to the logical-qubit scale at which we will test it against the classical solver, running today across seven hardware platforms. No advantage on present hardware is claimed.

Impact

Know when to plan for it
Baseline: open-ended vendor timelines
Condition: a measured cost law projected to a stated logical-qubit scale; a projection, not a measurement
No lock-in to one machine
Baseline: a solver tied to one vendor
Condition: the same methods run on seven platforms across four hardware families

The problem

Flow simulation is one of the largest compute bills in aerospace and automotive design. Buyers need to know when quantum could matter for it, not a promise that it already does.

Our approach

We built a solver for the linear transport step of a flow simulation, measured how its cost grows, and placed the problem sizes and logical-qubit counts at which it is worth testing against the classical solver. The same methods run on superconducting, neutral-atom, trapped-ion and photonic machines.

The evidence

  • Cost growth measured on the transport operator; the crossing scale is a forward projection from that measured law.
  • Runs on seven platforms: superconducting, neutral-atom, trapped-ion and photonic.
  • Not claimed: an advantage on present hardware or on standard benchmark flows.

Status: Prediction. A projection from a measured cost law. Platform reach is measured on hardware.

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