Configuration Optimization of Pre-Production Vehicles
summary
Reports a working solution for configuring pre-production fleets to cover ~14,000 tests with about 2000 vehicles, using a decoupled pipeline that first generates feasible car configurations then schedules tests, and compares two solvers: a simulated quantum pipeline with Dürr–Høyer Quantum Minimum Finding plus amplitude amplification (scaling requires fault‑tolerant machines), and a native‑hybrid local optimizer calling MAX‑SAT backends including NTT Coherent Ising Machine and D‑Wave. The classical MAX‑SAT implementation benchmarks on industrial data and shows significantly faster runtimes with acceptable solution quality.
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chapters (9)
- 0:00Introduction
- 2:21Problem Statement
- 7:20Monolithic Approach
- 9:39Decoupled Approach
- 11:10Classical Approach
- 12:44Quantum Approach
- 14:20Scaling
- 16:19Results
- 20:11Audience Questions
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