T1–T2W-MISRetail
Retail store-network siting with cannibalization
Place stores to maximize captured demand without cannibalizing each other — weighted coverage-conflict.
Sector
Retail / real estate
Likely buyer
Retail chains; real-estate planners
Hardware gate
Weighted → Vela
Taxonomy
cost-prohibitive × accelerating
Live demo — adiabatic sweep on 5 stores
Ω 9.4 · δ ±12.6 rad/µs · 4000 ns · R_b 9.1 µmStores within a cannibalization radius compete; atom size = revenue potential. Maximize captured demand.
Weighted instance: site values map to per-atom detuning — on hardware this needs Vela-class local addressing. Emulated exactly here.
GTM talk track
'Open the stores that capture the most demand without eating each other's lunch.'
OGSM — product operating frame
Objective
Demonstrate store siting on a market.
Goals
- One chain runs a metro
Strategies
- Map cannibalization + revenue value
Measures
- Captured demand
- Store count
OBR — outcome-based roadmap
| Horizon | Outcome we create | Buyer behavior change | Result we measure |
|---|---|---|---|
| Now | Chain sees siting on a metro | Prospect runs the emulated demo on their own instance data | Booked QPU-time evaluation or paid pilot |
| Next | Chain benchmarks a region | Prospect co-designs a scoped benchmark against their incumbent solver | Documented crossover curve; expansion to production instances |
| Later | Chain adopts for expansion | Prospect standardizes on the workflow or buys an on-prem system | Recurring QPU consumption / system sale; reference case |
Fit notes (honesty gate)
Proximity-conflict with dollar values per node.
Ready to run this on real hardware?
Emulation-verified today — the same program runs on a Pasqal QPU unchanged.