T2HAMAdvanced MaterialsAutomotiveEnergy & Utilities
Battery cathode & solid-state electrolyte modeling
Model battery materials at quantum accuracy — high value; native fit bounded by qubit count. BASF/BMW-adjacent.
Sector
Energy materials
Likely buyer
Battery makers; automotive OEMs; chemical companies
Named precedent
Pasqal × BASF, BMW materials collaborations
Hardware gate
Chemistry accuracy scales with qubits; near-term = reduced models
Taxonomy
cost-prohibitive × accelerating
No live demo yet — Hamiltonian-simulation runner on the roadmap; shares kernel with magnet screening. The paradigm-honest position: this use case is demonstrated with crossover analysis, not theater.
GTM talk track
'BASF and BMW already work with Pasqal on materials. Battery chemistry is the same simulation kernel.'
OGSM — product operating frame
Objective
Reuse the simulation kernel for an energy-materials buyer.
Goals
- One battery maker models a candidate system
Strategies
- Reduce the Hamiltonian to stay native
- Bundle with the magnet screening story
Measures
- Materials modeled
- Accuracy vs classical DFT
OBR — outcome-based roadmap
| Horizon | Outcome we create | Buyer behavior change | Result we measure |
|---|---|---|---|
| Now | Buyer sees a reduced-model simulation | Prospect runs the emulated demo on their own instance data | Booked QPU-time evaluation or paid pilot |
| Next | Buyer benchmarks a research question near crossover | Prospect co-designs a scoped benchmark against their incumbent solver | Documented crossover curve; expansion to production instances |
| Later | Buyer runs on QPU / partners | Prospect standardizes on the workflow or buys an on-prem system | Recurring QPU consumption / system sale; reference case |
Fit notes (honesty gate)
Shares the simulation kernel with #magnets and #hydrogen — build once, sell thrice.
Ready to run this on real hardware?
Emulation-verified today — the same program runs on a Pasqal QPU unchanged.