Accelerate materials discovery with Wipro’s quantum-powered simulations for next-gen innovation.
Designing next‑generation materials for batteries, semiconductors, catalysts, and advanced composites demands navigating an astronomically large design space, millions of candidate molecules, crystal structures, dopants, and process parameters. Classical methods such as Density Functional Theory (DFT) and Molecular Dynamics are foundational, but they scale poorly when electron correlation becomes significant or when screening must span vast combinatorial spaces. The result is slow iteration, limited exploration, and reduced predictive accuracy, key barriers to faster, more sustainable innovation.
Why Quantum Computing
Quantum computers natively simulate quantum systems, allowing us to approximate ground‑state energies and electronic properties that define stability, conductivity, magnetism, and reactivity. This reduces experimental iterations, shortens timetodiscovery, and enables exploration of complex chemistries previously out of reach.
Our Solution
To make the benefits tangible for enterprise R&D, we selected Lithium Hydride (LiH), a wellstudied benchmark molecule that balances simplicity with rich electronic structure. In our implementation, we used Variational Quantum Eigensolver (VQE), a hybrid algorithm that prepares a parameterized quantum state on a quantum backend and iteratively optimizes parameters on a classical optimizer to minimize the Hamiltonian’s expectation value. Based on comparison against Full Configuration Interaction (FCI) “truth” values and Hartree–Fock (HF) baselines, improved accuracy was observed from the hybrid quantum approach. This implies improving groundstate energy estimates and, by extension, stability ranking of candidate structures.
