VQE and quantum chemistry for materials property simulation in manufacturing R&D programmes.
Where DFT, molecular dynamics, and HPC hit accuracy ceilings.
Variational Quantum Eigensolver applied to materials problems. Key areas include: Ground-state energy calculation for molecular systems: VQE with UCCSD ansatz on small molecules (LiH, H2O, BeH2); Active space selection: choosing which orbitals to simulate on quantum hardware versus classical pre-processing; Noise impact on VQE convergence: error mitigation techniques for NISQ processors (zero-noise extrapolation, probabilistic error cancellation).
Accelerating candidate identification for manufacturing R&D. Key areas include: Quantum kernel methods for high-dimensional materials feature spaces (bandgap, tensile strength, thermal conductivity); Hybrid quantum-classical workflows: embedding quantum subroutines into existing HPC simulation pipelines; Cost-benefit analysis: when quantum simulation adds value versus classical DFT and machine learning potentials.
Facilitator-led VQE simulation of a target molecular system. Key areas include: Facilitator-led walkthrough: setting up a VQE calculation for a small molecule on a cloud quantum backend; Interpreting energy convergence plots and comparing quantum results against classical CCSD(T) reference values; Assessing qubit requirements and circuit depth for industrially relevant molecules (catalyst intermediates, polymer monomers).
Honest assessment of current hardware limitations and timeline to utility. Key areas include: Qubit count and coherence time requirements for production-scale materials simulation (100+ qubit active spaces); Error correction overhead: surface code thresholds and their impact on time-to-solution estimates; Structuring a first quantum simulation project: problem selection, vendor evaluation, and success metrics.
Q&A and Action Planning: this session covers the core principles and technical underpinnings relevant to the subject area.
Discuss this topic with senior peers.
