Tomono, Takao

写真a

Affiliation

Graduate School of Media and Governance 政策メディア研究科 （Shonan Fujisawa）

Position

Project Professor (Non-tenured)

E-mail Address

E-mail address

Telephone No.

044-271-5071
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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.
    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 IEEE SA (Standard for Quantum Computing Architecture) and several other international conferences.

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Other Affiliation 【 Display / hide 】

  • graduate school of Science and technology, 特任教授

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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

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Academic Background 【 Display / hide 】

  • 1980.04
    -
    1984.03

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

    University, Other, Doctoral course

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Academic Degrees 【 Display / hide 】

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

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

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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)

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Research Keywords 【 Display / hide 】

  • Photonic chip

  • Micro Fabrication

  • machine learning

  • quantum AI

  • Quantum photonics

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Research Themes 【 Display / hide 】

  • quantum Internet, 

    2023.12
    -
    Present

  • quantum AI, 

    2018.04
    -
    Present

  • quantum photonics, 

    1991.04
    -
    Present

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Books 【 Display / hide 】

  • 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

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Papers 【 Display / hide 】

  • Tensor Network-Based Continuous Variable Quantum Circuit Optimization for Preparation of GKP State

    Ryutaro Nagai, Takao Tomono

    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   385 - 386 2023

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

    　View Summary

    Tensor networks are highly promising methods for efficiently simulating quantum systems using classical computers. In recent years, the efficiency of tensor networks has been eagerly harnessed to simulate NISQ devices in the era of quantum supremacy. However, as of now, quantum advantage with NISQ devices has not been clearly demonstrated in practical applications. This provide impetus for us to intensify our effort in fault tolerant quantum computation (FTQC). Various physical systems and schemes for FTQC have been proposed, with GKP encoding in bosonic systems being one such example. The preparation of GKP states serves as an important building block for realizing FTQC with the GKP code. We introduce tensor networks for simulating and optimizing the GKP state preparation circuit. It is known that the generation of approximated GKP states is achievable through photon counting and post-selection applied to properly prepared multimode Gaussian states. However, it requires optimization of numerous circuit parameters, which is typically computationally challenging on classical computers due to the involvement of the photon counting measurement process. We attempt to utilize tensor networks for more efficient parameter optimization. Additionally, we explore further efficient approach in terms of tensor network structure. We propose a multi-cutoff dimension approach combined with a tree tensor network structure.

  • Shipping inspection trial of quantum machine learning toward sustainable quantum factory

    T Tomono, S Natsubori

    PHM Society Asia-Pacific Conference 4 (1)  2023

    Research paper (international conference proceedings), Lead author, Corresponding author, Accepted,  ISSN  2994-7219

    　View Summary

    <jats:p>In recent years, the diversification of consumer values has led to an increase in the number of small-quantity, high-mix products. For many manufacturing companies, shipping inspections of such products are of great importance. As all products have the same value, good and defective products need to be efficiently identified. Now, a promising future application of quantum technology is considered to be quantum machine learning. We believe that the quantum classifier for SVMs using quantum kernels is one of the areas where quantum advantages can be demonstrated. At present, there are few examples of quantum classifiers applied to real problems in manufacturing processes. In this study, we aim to build a classifier that can demonstrate the quantum advantage and compare SVMs using classical and quantum kernels with conventional ResNet. Initially, a binarised image was generated after image pre-processing. After principal component analysis and dimensionality reduction were performed on the images, SVM with kernels was carried out. The kernel-based SVMs was then compared with the conventionally implemented Residual neural network (ResNet) using an evaluation index: F1-score. The results showed that the F1-score of SVMs using classical kernels was equivalent to that of Resnet. In addition, SVMs using quantum kernels showed higher F1-score than ResNet. In addition, the impact of the feature map and principal components of the quantum kernel was also investigated. It was found that when the feature map became more complex, conversely, circuit generation took more time. It was also found that the principal components are highly relevant to the image and cannot lead to simple results. In the future, we plan to accumulate more experimental data, look for scenes where quantum machine learning can be used and apply it to the manufacturing field.  </jats:p>

  • 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.

  • Performance of quantum kernel on initial learning process

    Takao Tomono, Satoko Natsubori

    EPJ Quantum Technology （EPJ Quantum Technology)  9 （ 1 ）  2022.12

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

    　View Summary

    For many manufacturing companies, the production line is very important. In recent years, the number of small-quantity, high-mix products have been increasing, and the identification of good and defective products must be carried out efficiently. At that time, machine learning is a very important issue on shipping inspection using small amounts of data. Quantum machine learning is one of most exciting prospective applications of quantum technologies. SVM using kernel estimation is one of most popular methods for classifiers. Our purpose is to search quantum advantage on classifier to enable us to classifier in inspection test for small size datasets. In this study, we made clear the difference between classical and quantum kernel learning in initial state and propose analysis of learning process by plotting ROC space. To meet the purpose, we investigated the effect of each feature map compared to classical one, using evaluation index. The simulation results show that the learning model construction process between quantum and classical kernel learning is different in initial state. Moreover, the result indicates that the learning model of quantum kernel is the method to decrease the false positive rate (FPR) from high FPR, keeping high true positive rates on several datasets. We demonstrate that learning process on quantum kernel is different from classical one in initial state and plotting to ROC space graph is effective when we analyse the learning model process.

  • Optimization of non-Gaussian state generation using tensor networks and automatic differentiation

    Ryutaro Nagai, Takao Tomono

    Proceedings - 2022 IEEE International Conference on Quantum Computing and Engineering, QCE 2022 （Proceedings - 2022 IEEE International Conference on Quantum Computing and Engineering, QCE 2022)     818 - 819 2022

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

    　View Summary

    Tensor networks have been eagerly developed in recent years as a simulation method for quantum computation on classical computers. The efficiency of tensor networks for simulating quantum computation has been shown by solving a task designed for demonstrating quantum computing supremacy. However, such a task is not useful for real application. Therefore, there is the room for research of finding specific applications suitable for exploiting tensor network simulation. As such an application, we propose optimization of circuits to generate non- Gaussian states. Non-Gaussian state is one of the crucial elements for achieving universal continuous variable quantum computation. We consider this problem as a variational optimization of optical circuits to generate desired non-Gaussian states. Tensor networks efficiently simulate the optical circuits, and their compatibility with automatic differentiation provides a heuristic way to optimize the circuits. We propose and report the result of our approach that combines tensor networks and automatic differentiation to perform variational optimization of circuits such that the desired non-Gaussian state is approximately generated. The complexity of contracting tensor networks is scaled by degree of entanglement, so we consider relatively shallow optical circuit. Such a restriction is beneficial for considering devices without error correction method, which are accessible in near-term.

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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

  • 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.

  • 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.

  • 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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Intellectual Property Rights, etc. 【 Display / hide 】

  • 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

  • Counterfeit prevention device and production method thereof

    Date applied：   2013.11 

    Date announced： 特開2015089638-A  2015.05 

    Patent

  • Forgery prevention device and authenticity determination method thereof

    Date applied：   2013.09 

    Date announced： 特開2015069070A  2015.04 

    Patent

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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 】

  • 2023.04
    -
    Present

    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

  • 2006.04
    -
    Present

    Workshop on FPD Manufacturing, Materials and Components, International Display Workshop

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