rhumb.
Everything upstream of the solver is deterministic engineering: a live twin, a REST contract, a cost matrix, a QUBO. Everything downstream is swappable. That is what makes the platform quantum-ready without betting the product on quantum hardware.
Held-Karp exact solutions at small scale; nearest-neighbour + 2-opt beyond. Always-on honesty baseline — every other backend must beat it or say so.
Path-integral Monte Carlo via neal / OpenJij — the method used for parameter tuning in the QATO research programme.
Toshiba's SB algorithm on PyTorch — hundreds of parallel solution agents per GPU. Runs on a laptop (Apple silicon) or scales unchanged to CUDA clusters such as Hartree's Mary Coombs system.
The same QUBO submits directly to D-Wave Advantage2 via Ocean/Leap. When quantum hardware wins on a problem class, swapping it in is configuration, not engineering. And the formulation carries to gate-model machines too — we studied QAOA on fault-tolerant hardware with PsiQuantum in QuEST.
Every stochastic backend's best tour is refined classically (2-opt) before acceptance — the hybrid pattern that consistently beats either half alone.
A benchmark harness prices every backend on identical instances — cost, optimality gap, wall time, feasibility rate — from 10-stop rounds to 3,600-variable QUBOs beyond early quantum chip capacity.
Stop ordering becomes a grid of binary switches — xi,p = "stop i sits at position p" — with drive times as couplings and constraint penalties that make every illegal schedule more expensive than any legal one:
Identical formulation across the browser demo, the Python service and the quantum backends. See it working — and the maths behind each strategy — in the playable delivery twin.
Depot rounds of 10–20 stops solve in milliseconds in a browser tab. Fleet-wide joint optimisation, signal-phase co-optimisation and disruption replanning grow into tens of thousands of binary variables — the regime where GPU annealing-class solvers and, eventually, quantum annealers earn their keep, and the scale D-Wave's current systems and announced roadmap are aimed at. Industry pilots by Volkswagen, Toyota and DENSO already report 6–26% gains in routing and traffic flow. Because the cost model never changes, today's benchmarks remain tomorrow's acceptance tests — a 10% saving is roughly ten thousand driver-hours a year for a hundred-van depot.