Virtual Screening of Chemical Space based on Quantum Annealing

Abstract

For searching a new chemical material which satisfies the target characteristic value, for example emission wavelength, many cut and trial of experiments/calculations are required since the chemical space is astronomically large (organic molecules generates >10^60 candidates). Extracting feature importance is a method to reduce the chemical space, and limiting the search space to those features leads to shorter development time. Quantum computer can generate sampling data faster than classical computers, and this property is utilized to extract feature importance. In this paper, quantum annealer was used as a sampler to make data for extracting feature importance of material properties. By screening the chemical space with feature importance, it was found that the chemical space can be reduced to less than 1 percent. This result suggests that the acceleration of material research can be achievable.

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• Online calibration scheme for training restricted Boltzmann machines with quantum annealing
• Random Postprocessing for Combinatorial Bayesian Optimization
• Subgradient Method using Quantum Annealing for Inequality-Constrained Binary Optimization Problems