Tackling the challenge of a huge materials science search space with quantum-inspired annealing

Abstract

Efficient screening of chemicals is essential for exploring new materials. However, the search space is astronomically large, making calculations with conventional computers infeasible. For example, an $N$-component system of organic molecules generates >$10^{60N}$ candidates. Here, a quantum-inspired annealing machine is used to tackle the challenge of the large search space. The prototype system extracts candidate chemicals and their composites with desirable parameters, such as melting temperature and ionic conductivity. The system can be at least $10^4$-$10^7$ times faster than conventional approaches. Such exponential acceleration is critical for exploring the enormous search space in virtual screening.

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• Lossy compression of matrices by black-box optimisation of mixed integer nonlinear programming
• Optimization of ionic configurations in battery materials by quantum annealing
• Computing Canonical Averages with Quantum and Classical Optimizers: Thermodynamic Reweighting for QUBO Models of Physical Systems
• Continuous black-box optimization with quantum annealing and random subspace coding
• Simulating charging characteristics of lithium iron phosphate by electro-ionic optimization on a quantum annealer