What problem does it solve? Automotive engineering problems like battery electrochemistry simulation, crash structure optimization, and post-quantum V2X security exceed the practical limits of classical computing, and engineers need structured guidance on where quantum algorithms genuinely help versus where classical methods remain superior. ## Core Features & Use Cases - Quantum Battery Simulation: Use VQE and quantum phase estimation to compute molecular ground states, intercalation voltages, and electrolyte decomposition pathways for cathode and electrolyte materials. - Post-Quantum V2X Cryptography: Migrate V2X message signing from ECDSA to NIST-standardized ML-DSA, ML-KEM, and SLH-DSA with hybrid certificates and a phased crypto-agility migration plan. - Crash Simulation & Materials Design: Apply HHL linear solvers, QUBO topology optimization, and quantum embedding to accelerate FEA subproblems and screen alloys, catalysts, and semiconductors. - Use Case: A battery engineer needs lithium intercalation energetics for a candidate cathode material; the skill walks through generating the qubit Hamiltonian with Qiskit Nature, running ADAPT-VQE, and validating against classical CCSD(T) benchmarks. ## Quick Start Ask the agent to design a VQE workflow for computing the ground state energy of a lithium-ion battery electrolyte molecule and validate it against classical benchmarks.
