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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.

A 42 year-old man with lumbago and fever had hydronephrosis due to a mass surrounding the abdominal aorta, associated with pachymeningitis and a suprasellar tumor. He was given a diagnosis of multifocal fibrosclerosis, involving both retroperitoneal and intracranial fibrosis. He responded to treatment with corticosteroid and cyclophosphamide. The intracranial lesions seemed to be rare, but important manifestations of the systemic disease, and were clearly visualized by magnetic resonance imaging. The phenotype of the infiltrated lymphocytes was CD4+, supporting the hypothesis that autoimmunity was involved pathogenetically. We propose a comprehensive category termed "inflammatory fibrotic disease" including both systemic and localized diseases.(Internal Medicine 42: 1006-1010, 2003)