QaaS: Quantum-Assisted Route Optimization Pilot for a Logistics Firm
QaaS Pilot — Quantum-Assisted Vehicle Routing Optimization (3-month pilot)
Objective
Demonstrate measurable delivery-cost and time improvements by integrating a quantum-assisted optimizer (hybrid classical-quantum pipeline) into one logistics route cluster (50–200 stops). Goal: reduce total route distance or makespan by 5–12% vs current heuristic baseline during the pilot window.
Business case
Optimization is a near-term practical quantum application (NISQ-era hybrid algorithms like QAOA / VQE for combinatorial problems). Indian logistics firms have clear KPIs (fuel cost, driver hours, on-time delivery) where even single-digit improvements scale to large annual savings. Use pilot results to justify subscription QaaS deployment.
Scope & deliverables (3 months)
Data intake & baseline — collect route data, constraints, service windows; run classical baseline (current optimizer).
Build hybrid optimizer (classical pre-processing + parameterized variational QAOA on simulator / small hardware).
A/B test on simulated/rolling live subset (non-critical route batch) and measure KPIs.
Final report with metrics, cost/benefit analysis, recommended rollout plan and pricing model.
Technical approach & stack
Llightweight dashboard (React/Tailwind) for route input, compare results.
Python (Qiskit + Pennylane) on cloud; start on simulators → test on IBM/Braket hardware via cloud.
QAOA / hybrid local search + classical heuristics for scalability.
Comply with firm’s data rules; no sensitive personal data used; results anonymized.
KPIs - Success metrics
- % reduction in total route distance or makespan (primary)
- % decrease in fuel/time per route
- Compute cost & wall-clock time for optimizer (practicality measure)
- ROI projection for 12 months if rolled out to X routes
