T3QUBO-EEnergy & Utilities
Grid load balancing / unit commitment
Which generators to run when, at scale — massive but embedding-taxed; hybrid decomposition only.
Sector
Energy / grid
Likely buyer
Grid operators; ISOs
Hardware gate
Hybrid; classical MILP strong
Taxonomy
throughput-limited × cost-collapsing
No live demo yet — full unit commitment embeds poorly; classical MILP wins. The paradigm-honest position: this use case is demonstrated with crossover analysis, not theater.
GTM talk track
Honesty gate: recommend classical for the core; explore quantum subproblems.
OGSM — product operating frame
Objective
Watching brief with subproblem exploration.
Goals
- Identify any native subgraph
Strategies
- Decompose, test small
Measures
- N/A near-term
OBR — outcome-based roadmap
| Horizon | Outcome we create | Buyer behavior change | Result we measure |
|---|---|---|---|
| Now | Educate on boundary | Prospect runs the emulated demo on their own instance data | Booked QPU-time evaluation or paid pilot |
| Next | Test a subproblem | Prospect co-designs a scoped benchmark against their incumbent solver | Documented crossover curve; expansion to production instances |
| Later | Reassess | Prospect standardizes on the workflow or buys an on-prem system | Recurring QPU consumption / system sale; reference case |
Fit notes (honesty gate)
Large but embeds poorly; classical MILP is hard to beat.
Ready to run this on real hardware?
Emulation-verified today — the same program runs on a Pasqal QPU unchanged.