小細胞肺がんモデルに対する放射免疫療法の病理学的解析および化学療法との併用による治療効果に関する検討

学位の種別: 課程博士審査委員会委員 : (主査)東京大学教授 宮川 清, 東京大学教授 高戸 毅, 東京大学准教授 門野 岳史, 東京大学講師 高尾 英正, 東京大学講師 榎本 敦University of Tokyo(東京大学

Demonstration of a bosonic quantum classifier with data re-uploading

In a single qubit system, a universal quantum classifier can be realised using the data-reuploading technique. In this study, we propose a new quantum classifier applying this technique to bosonic systems and successfully demonstrated it using silicon optical integrated quantum circuits. We established a theory of quantum machine learning algorithm applicable to bosonic systems and implemented a programmable optical circuit combined with an interferometer. Learning and classification using part of the implemented optical quantum circuit with uncorrelated two-photons resulted in a classification with a reproduction rate of approximately 94\% in the proof of principle experiment. As this method can be applied to arbitrary two-mod N-photon system, further development of optical quantum classifiers, such as extensions to quantum entangled and multi-photon states, is expected in the future.Comment: 9 pages, 4 figure