T2W-MISPharmaceuticals & Healthcare

Clinical-trial site & cohort optimization

Select trial sites balancing geography and covariate coverage without redundancy — geographic + covariate conflict graph.

Sector

Healthcare / clinical operations

Likely buyer

CROs; pharma clinical ops

Hardware gate

Weighted → Vela

Taxonomy

resource-constrained × accelerating

Ω 9.4 · δ ±12.6 rad/µs · 4000 ns · R_b 9.1 µm

Trial sites with overlapping catchments are redundant; atom size = enrollment value. Maximize distinct coverage.

Weighted instance: site values map to per-atom detuning — on hardware this needs Vela-class local addressing. Emulated exactly here.

GTM talk track

'Pick the trial sites that cover the most distinct patient populations without overlap.'

OGSM — product operating frame

Objective

Demonstrate site selection on trial geography.

Goals

Strategies

Measures

Horizon	Outcome we create	Buyer behavior change	Result we measure
Now	CRO sees selection on a map	Prospect runs the emulated demo on their own instance data	Booked QPU-time evaluation or paid pilot
Next	CRO benchmarks a real protocol	Prospect co-designs a scoped benchmark against their incumbent solver	Documented crossover curve; expansion to production instances
Later	CRO adopts for planning	Prospect standardizes on the workflow or buys an on-prem system	Recurring QPU consumption / system sale; reference case

Fit notes (honesty gate)

Geographic + covariate conflicts; weighted by enrollment value.

Ready to run this on real hardware?

Emulation-verified today — the same program runs on a Pasqal QPU unchanged.