KEIO RESEARCHERS INFORMATION SYSTEM

Profile 【 Display / hide 】

Profile 【 Display / hide 】

• Bachelor's degree from the University of Tsukuba in 1984. Ph.D. degree in quantum optics from Keio University in 1998. Since 1984, he has worked at Sharp Corporation for 5 years, Fuji Xerox for 11 years, Samsung Electronics for 3.5 years, and Toppan Holdings for 20 years, before starting work at Keio University in December 2023.During this time, in addition to research, he was also involved in management and business promotion as the head of the company's corporate planning department for over three years. Prior to 2013, he worked in the field of photonics, quantum optics, and semiconductor/microfabrication research. Since 2013 he had been working on computer vision (optical metrology) and since 2018 on quantum information (quantum machine learning, quantum optics).
  Products he has developed include TFT-driven printer heads for A0 size printers, lens sheets for rear projection televisions, and medical microneedles.
  
  Academic societies: Member of IEEE Senior Member (Computer Society, Photonics Society), Japan Society for Artificial Intelligence (JSAI), Japan Society of Applied Physics (JSAP), and Optical Society of Japan (OSJ).
  Committee activities: Concurrently serves as a committee member for several international conferences.　Ai-map committee member of JSAI.

Other Disclosed Information 【 Display / hide 】

Other Disclosed Information 【 Display / hide 】

• I currently conduct 1-2 peer-reviewed papers per month (machine learning and quantum machine learning).

Other Affiliation 【 Display / hide 】

Other Affiliation 【 Display / hide 】

• graduate school of Science and technology, 特任教授

Career 【 Display / hide 】

Career 【 Display / hide 】

• 2023.12

  -

  Present

  Keio University, Graduate School of Media and Governance, Project Professor

• 2024.01

  -

  Present

  Keio University, Graduate School of Science and Technology, Project Professor

Academic Background 【 Display / hide 】

Academic Background 【 Display / hide 】

• 1980.04

  -

  1984.03

  University of Tsukuba, College of Engineering Sciences, Third Cluster of Colleges

  University, Other, Doctoral course

Academic Degrees 【 Display / hide 】

Academic Degrees 【 Display / hide 】

• Ph.D., Keio University, Dissertation, 1998.02

  Study on Molecular Orientation and Nonlinear Optical Properties of Cyclobutendione-based Organic Materials

Research Areas 【 Display / hide 】

Research Areas 【 Display / hide 】

• Natural Science / Mathematical physics and fundamental theory of condensed matter physics (Quantum AI)

• Manufacturing Technology (Mechanical Engineering, Electrical and Electronic Engineering, Chemical Engineering) / Electron device and electronic equipment (Photonic chip)

• Informatics / Intelligent robotics (Machine larning)

Research Keywords 【 Display / hide 】

Research Keywords 【 Display / hide 】

• Photonic chip

• Micro Fabrication

• machine learning

• quantum AI

• Quantum photonics

Research Themes 【 Display / hide 】

Research Themes 【 Display / hide 】

• quantum AI, 

  2018.04

  -

  Present

• quantum photonics, 

  1991.04

  -

  Present

Books 【 Display / hide 】

Books 【 Display / hide 】

• AI Problems Map and Business Application of AI Map

  吉岡健, 友野孝夫, 友野孝夫, 一般社団法人 人工知能学会, 2023.07

  • Access to Document (DOI)

• 有機非線形光学材料の開発と応用

  中西, 八郎, 小林, 孝嘉, 中村, 新男, 梅垣, 真祐, シーエムシー, 2001.08,  Page： xiii, 558p

  Scope: 7.シクロブテンジオン環を有する新しい有機非線形光学材料

Papers 【 Display / hide 】

Papers 【 Display / hide 】

• The discriminative ability on anomaly detection using quantum kernels for shipping inspection

  Takao Tomono and Kazuya Tsujimura

  epj Quantum Technology (Springer Science and Business Media LLC)  12 ( 1 )  2025.03

  Research paper (scientific journal), Joint Work, Lead author, Corresponding author, Accepted,  ISSN  2662-4400

  　View Summary

  We aim to use quantum machine learning to detect various anomalies in image inspection by using small size data. Assuming the possibility that the expressive power of the quantum kernel space is superior to that of the classical kernel space, we are studying a quantum machine learning model. Through trials of image inspection processes not only for factory products but also for products including agricultural products, the importance of trials on real data is recognized. In this study, training was carried out on SVMs embedded with various quantum kernels on a small number of agricultural product image data sets collected in the markets. The quantum kernels prepared in this study consisted of a smaller number of rotating gates and control gates. The F1 scores for each quantum kernel showed a significant effect of using CNOT gates. After confirming the results with a quantum simulator, the usefulness of the quantum kernels was confirmed on a quantum computer. Learning with SVMs embedded with specific quantum kernels showed significantly higher values of the AUC compared to classical kernels. The reason for the lack of learning in quantum kernels is considered to be due to kernel concentration or exponentialconcentration similar to the Baren plateau. The reason why the F1 score does not increase as the number of features increases is suggested to be due to exponential concentration, while at the same time it is possible that only certain features have discriminative ability. Furthermore, it is suggested that controlled Toffoli gate may be a promising quantum kernel component.

  • Access to Document (DOI)

• Liquid crystal waveguide film and its application to smart glasses

  Takao Tomono, Rumiko Yamaguchi

  Discover Electronics (Springer Science and Business Media LLC)  1 ( 1 )  2024.06

  Lead author, Last author, Corresponding author, Accepted,  ISSN  2948-1600

  　View Summary

  Abstract
  
  Liquid crystal waveguide film that we can control through wide visible wavelength has been proposed. Recently, small optical system is needed for various industrial application. Among the application, liquid crystal is the material for controlling optical ray. The application includes optical switch and display by controlling birefringence. These applications include AR/VR display, for manufacturing, inspection, and so on. In this study, we have investigated liquid crystal (LC) waveguide film that light is emitted from the waveguide film on glasses toward the eye when the power is turned on. The cut-off of waveguide film is based on liquid crystal switch using refractive index anisotropy. We select smart glasses as one of the applications for AR/VR display. The potential of LC waveguide films is demonstrated by simulating the propagation characteristics of waveguides film, LC reorientations, and diffraction gratings in the field of RGB wavelength range. By using the waveguide structure, the film thickness can be configured to be less than 1 mm. We can expect the liquid crystal waveguide has much application to optical integrated circuit as other application except for smart glasses.

  • Access to Document (DOI)

• Quantum kernel learning Model constructed with small data

  T Tomono, K Tsujimura

  arXiv preprint arXiv:2412.00783  2024

  Lead author, Last author, Corresponding author, Accepted

• Discover Electronics

  T Tomono, R Yamaguchi

   2024

  Lead author, Last author, Corresponding author, Accepted

• Quantum Kernels for Difficult Visual Discrimination

  Takao Tomono, Kazuya Tsujimura, Takumi Godo

  Proceedings - 2023 IEEE International Conference on Quantum Computing and Engineering, QCE 2023 (Proceedings - 2023 IEEE International Conference on Quantum Computing and Engineering, QCE 2023)  2   262 - 263 2023

  Research paper (international conference proceedings), Lead author, Corresponding author, Accepted

  　View Summary

  Quantum machine learning has attracted much attention in recent years as one of the applications of quantum computing. In particular, classification using quantum kernels has attracted attention as a means of efficient classification by mapping to a feature space. We aim to use quantum machine learning to identify good and defective products in images including factory products, building, and farm products. Though a fruit apple looks delicious, it has a vine crack inside when it is split in two, it loses its commercial value. It is very difficult to distinguish normal apples from apples with internal vine cracks which are not visible on the exterior, using photographs of the exterior. In this study, an attempt was made to classify internal defects with classical and quantum kernels using binarized images of apples, which are difficult to distinguish with the naked eye. As a result, the accuracy of the classical kernel was less than 0.75. However, with the quantum kernel, an accuracy of over 0.94 could be obtained. The performance of the quantum kernel varied significantly depending on its type. We found that quantum kernel circuits having Hadamard, and control Ry gate have affected the construction of the learning model. We have demonstrated that high accuracy can be achieved by using quantum kernels for the classification of images that are difficult to discriminate visually.

  • Access to Document (DOI)

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Reviews, Commentaries, etc. 【 Display / hide 】

Reviews, Commentaries, etc. 【 Display / hide 】

• AI Problems Map and Business Application of AI Map

  吉岡健, 友野孝夫, 友野孝夫

  人工知能 (一般社団法人 人工知能学会)  38 ( 4 ) 529 - 538 2023.07

  Last author, Corresponding author,  ISSN  2188-2266

  • Access to Document (DOI)

• Inspection trail with factory data on quantm kernel learning

  TOMONO Takao, NATSUBORI Satoko

  Proceedings of the Annual Conference of JSAI (The Japanese Society for Artificial Intelligence)  JSAI2023   3Xin475 - 3Xin475 2023

  Lead author, Last author, Corresponding author,  ISSN  2758-7347

  　View Summary

  Machine learning classifiers have been used in medicine, factory inspections, and automated driving. Support Vector Machines (SVMs), one of the classifiers, are useful and have been used in various situations. In particular, kernel methods are very important for nonlinear and unsolvable classification. On the other hand, quantum machine learning has received much attention in recent years, but its specific evaluation has not been done much. In this study, we applied quantum kernel learning to the factory inspection process. As a result, it showed higher performance than classical kernel learning. This time, the image data was preprocessed, binarized, and then subjected to principal component analysis. Although the cumulative contribution rate was 75%, the accuracy was over 97% when performing quantum kernel learning. The accuracy of 93% is also obtained by classical kernel learning. Kernel learning is known to depend on the properties of the dataset, but in the future, we would like to accumulate data on what kind of datasets show the superiority of quantum.

  • Access to Document (DOI)

• Performance evaluation of quantum kernel machine learning

  TOMONO Takao, NATSUBORI Satoko, IMAIZUMI Katsumi

  Proceedings of the Annual Conference of JSAI (The Japanese Society for Artificial Intelligence)  JSAI2022   4Yin250 - 4Yin250 2022

  Lead author, Last author, Corresponding author,  ISSN  2758-7347

  　View Summary

  Machine learning classifiers have been used in medicine, factory inspections, and automated driving. Support Vector Machines (SVMs), one of the classifiers, are particularly useful and have been used in various situations. In particular, kernel methods are very important for nonlinear and unsolvable classification. On the other hand, quantum machine learning has received much attention in recent years, but its specific evaluation has not been done much. In this study, we examined the process of building a learning model for classification using a heart disease data set. As a result, we found that the classical kernel method is a method to build a learning model by improving the true positive rate from a random model, while the quantum kernel method is a method to reduce the false positive rate from high true positive rate and false positive rate. In summary, we have demonstrated for the first time the process of quantum circuit learning by using the ROC space. Furthermore, we were able to construct a learning model using a quantum kernel with higher accuracy than that constructed by the classical kernel method.

  • Access to Document (DOI)

• Photonics quantum computing using tensor network 2: Application for real problems

  永井隆太郎, 友野孝夫

  応用物理学会春季学術講演会講演予稿集(CD-ROM) 69th 2022

  Research paper, summary (national, other academic conference), Last author, Corresponding author,  ISSN  2436-7613

• Efficient Optimization Method Using Tensor Networks for Circuit to Generate non-Gaussian States

  永井隆太郎, 友野孝夫

  Optics & Photonics Japan講演予稿集(CD-ROM) 2022 2022

  Lead author, Last author, Corresponding author

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Presentations 【 Display / hide 】

Presentations 【 Display / hide 】

• 量子機械学習による音声異常検知の試み -工場の導入を目指した基礎 検討-

  Takao Tomono, Kazuya Tsujimura

  2025年第72回応用物理学会春季学術講演会, 

  2025.03

  , 

  Oral presentation (general)

• Inspection trail with factory data on quantm kernel learning

  TOMONO Takao, NATSUBORI Satoko

  Proceedings of the Annual Conference of JSAI, 

  2023

  , 

  The Japanese Society for Artificial Intelligence

  　View Summary

  Machine learning classifiers have been used in medicine, factory inspections, and automated driving. Support Vector Machines (SVMs), one of the classifiers, are useful and have been used in various situations. In particular, kernel methods are very important for nonlinear and unsolvable classification. On the other hand, quantum machine learning has received much attention in recent years, but its specific evaluation has not been done much. In this study, we applied quantum kernel learning to the factory inspection process. As a result, it showed higher performance than classical kernel learning. This time, the image data was preprocessed, binarized, and then subjected to principal component analysis. Although the cumulative contribution rate was 75%, the accuracy was over 97% when performing quantum kernel learning. The accuracy of 93% is also obtained by classical kernel learning. Kernel learning is known to depend on the properties of the dataset, but in the future, we would like to accumulate data on what kind of datasets show the superiority of quantum.

• Efficient Optimization Method Using Tensor Networks for Circuit to Generate non-Gaussian States

  永井隆太郎, 友野孝夫

  Optics & Photonics Japan講演予稿集(CD-ROM), 

  2022

• Performance evaluation of quantum kernel machine learning

  TOMONO Takao, NATSUBORI Satoko, IMAIZUMI Katsumi

  Proceedings of the Annual Conference of JSAI, 

  2022

  , 

  The Japanese Society for Artificial Intelligence

  　View Summary

  Machine learning classifiers have been used in medicine, factory inspections, and automated driving. Support Vector Machines (SVMs), one of the classifiers, are particularly useful and have been used in various situations. In particular, kernel methods are very important for nonlinear and unsolvable classification. On the other hand, quantum machine learning has received much attention in recent years, but its specific evaluation has not been done much. In this study, we examined the process of building a learning model for classification using a heart disease data set. As a result, we found that the classical kernel method is a method to build a learning model by improving the true positive rate from a random model, while the quantum kernel method is a method to reduce the false positive rate from high true positive rate and false positive rate. In summary, we have demonstrated for the first time the process of quantum circuit learning by using the ROC space. Furthermore, we were able to construct a learning model using a quantum kernel with higher accuracy than that constructed by the classical kernel method.

• Photonics quantum computing using tensor network 2: Application for real problems

  永井隆太郎, 友野孝夫

  応用物理学会春季学術講演会講演予稿集(CD-ROM), 

  2022

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Intellectual Property Rights, etc. 【 Display / hide 】

Intellectual Property Rights, etc. 【 Display / hide 】

• Anomaly detection system, learning device, anomaly detection device, learning method, anomaly detection method, and program

  Date applied： 特願2025-031708  2025.02 

  Patent

• Optical Switch

  Date applied： 特願2025-020126  2025.02 

  Patent

• Power generator, and power generation method

  Date applied：   2018.07 

  Date announced： 特開2020022222-A  2020.02 

  Patent

• 2017032409

  Date applied：   2015.07 

  Date announced： 特開2017032409 A  2017.02 

  Patent

• Stereoscopic image display body

  Date applied：   2013.11 

  Date announced： 特開2015099187-A  2015.05 

  Patent

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Media Coverage 【 Display / hide 】

Media Coverage 【 Display / hide 】

• 量子カーネルを活用した高精度な異常検知技術を実現 ―少量データでの量子機械学習の有効性を実証し、製造DXを推進―

  (日本経済新聞、PR TIMESほか) , 2025.03

  　View Summary

  慶應義塾大学（所在地：東京都港区、塾長：伊藤公平）と、TOPPANホールディングス（本社：東京都文京区、代表取締役社長CEO：麿 秀晴）は、少量データでの農産物の品質検査における量子機械学習の有効性を実証し、従来の古典的機械学習手法と比較して検出精度が向上することを示しました。本研究成果は、2025年3月21日（日本時間）量子技術の国際科学ジャーナル「EPJ Quantum Technology」に掲載されました。

Academic Activities 【 Display / hide 】

Academic Activities 【 Display / hide 】

• Institute of Physics

  2023.12

  -

  Present

  　View Summary

  reviwer of
  machine Learning: Science and Technology
  Qauntum Science and Technology
  New Journal of physics
  

Memberships in Academic Societies 【 Display / hide 】

Memberships in Academic Societies 【 Display / hide 】

• IEEE computer society, 

  2018.10

  -

  Present

• IEEE Photonics Society, 

  2018.10

  -

  Present

• The Japanese Society for Artificial Intelligence, 

  2018.04

  -

  Present

• IEEE, 

  2016.04

  -

  Present

• The Optical Society of Japan, 

  2015.01

  -

  Present

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Committee Experiences 【 Display / hide 】

Committee Experiences 【 Display / hide 】

• 2025.01

  -

  Present

  Program committee, Gavriel Salvendy International Symposium on Frontiers in Industrial Engineering Purdue Quantum AI (PQAI)

• 2024.05

  -

  Present

  Program committee, QAIO

• 2023.04

  -

  2024.12

  IEEE SA Standard for Quantum Computing Architecture, IEEE

• 2020.10

  -

  Present

  Comittee of AI map, The Japanese Scoiety for artificial intelligence

• 2009.04

  -

  Present

  Workshop on Flexible electronics, International Display Workshop

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