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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| QIT (2nd) |
2024-05-28 - 2024-05-30 |
Ibaraki | AIST Tsukuba | [Poster Presentation]
Analysis of interaction networks of qubits and quantum feature maps in a quantum machine learning model Aoi Hayashi (SOKENDAI/OIST/NII), Akitada Sakurai, William J. Munro (OIST), Kae Nemoto (OIST/NII) |
(To be available after the conference date) [more] | |
| DC, CPSY, IPSJ-SLDM, IPSJ-EMB, IPSJ-ARC [detail] | 2024-03-23 11:45 |
Nagasaki | Ikinoshima Hall (Primary: On-site, Secondary: Online) |
Evaluating composition of quantum circuit and learnability in quantum neural network with NISQ devices Naoki Marumo (Waseda Univ.), Yasutaka Wada (Meisei Univ.), Kazunori Ueda, Keiji Kimura (Waseda Univ.)  CPSY2023-52 DC2023-118 |
The more numbers of repeat of Ansatz and the more qubit entangling improve learnability of quantum machine learning by v... [more] | CPSY2023-52 DC2023-118 pp.82-87 |
| QIT (2nd) |
2023-12-18 14:30 |
Okinawa | OIST (Primary: On-site, Secondary: Online) |
Advantage of Quantum Machine Learning from General Computational Advantages Hayata Yamasaki, Natsuto Isogai, Mio Murao (UTokyo) |
Demonstrating the existence of general learning problems where machine learning using quantum computers exhibits rigorou... [more] | |
| QIT (2nd) |
2023-12-17 17:30 |
Okinawa | OIST (Primary: On-site, Secondary: Online) |
[Poster Presentation]
Sparse identification of quantum dynamics via quantum circuit learning Yusei Tateyama, Yuzuru Kato (FUN) |
Sparse Identification of Nonlinear Dynamics (SINDy) is a data-driven method for estimation and prediction of nonlinear d... [more] | |
| QIT (2nd) |
2023-12-17 17:30 |
Okinawa | OIST (Primary: On-site, Secondary: Online) |
[Poster Presentation]
Analysis of Discrete Modern Hopfield Networks in Open Quantum System Takeshi Kimura, Kohtaro Kato (Nagoya Univ.) |
Hopfield Networks, a well-known model of associative memory, defines energy function and time evolution.Recently, a new ... [more] | |
| QIT (2nd) |
2023-12-17 17:30 |
Okinawa | OIST (Primary: On-site, Secondary: Online) |
[Poster Presentation]
Quantum Circuit Preparation for Loading Probability Distributions Using Label Swapping Yuichi Sano (Kyoto Univ.), Ikko Hamamura (IBM) |
Quantum state preparation is an essential subroutine in many quantum algorithms, such as quantum algorithm for Monte Car... [more] | |
| QIT (2nd) |
2023-12-17 17:30 |
Okinawa | OIST (Primary: On-site, Secondary: Online) |
[Poster Presentation]
Data-distributed machine-learning method for neural networks using quantum annealing Kosuke Nakanishi (Kyoto Univ.) |
A method for learning neural networks using quantum annealing (CQNN) has been proposed in previous research. This method... [more] | |
| QIT (2nd) |
2023-12-17 17:30 |
Okinawa | OIST (Primary: On-site, Secondary: Online) |
[Poster Presentation]
Quantum Reservoir Computing Utilizing Quantum Chaotic Systems with Heisenberg XXZ Spin Chains Shu Komatsugawa, Akihisa Tomita, Atsushi Okamoto (Hokkaido Univ.) |
Reservoir Computing (RC) is a machine learning method that aims to achieve both high learning performance and low learni... [more] | |
| RISING (3rd) |
2023-10-31 09:45 |
Hokkaido | Kaderu 2・7 (Sapporo) | [Poster Presentation]
Task Offloading for High-accuracy Computing in Mobile Edge-Quantum Computing Shimba Nozaki, Takuji Tachibana (Univ. Fukui) |
Because future applications with machine learning and AI require more sophisticated and faster computational processing... [more] |
|
| QIT (2nd) |
2023-05-30 10:00 |
Kyoto | Katsura Campus, Kyoto University | A new initial distribution for qGAN to load probability distributions Yuichi Sano, Ryosuke Koga (Kyoto Univ.), Masaya Abe, Kei Nakagawa (Nomura Asset Management) |
Quantum computers are gaining attention for their ability to solve certain problems faster than classical computers, and... [more] | |
| QIT (2nd) |
2023-05-29 16:30 |
Kyoto | Katsura Campus, Kyoto University | [Poster Presentation]
Improving the Performance of Quantum GANs through Label Replacement Ryosuke Koga, Yuichi Sano (Kyoto Univ.), Ikko Hamamura (IBM) |
Instead of Monte Carlo simulations for numerical integration, we can use a quantum expectation estimation algorithm, whi... [more] | |
| RCC, ISEC, IT, WBS | 2023-03-14 15:45 |
Yamaguchi | (Primary: On-site, Secondary: Online) |
Improvement of the Performance for Quantum Neural Network Classifiers based on Optimal Quantum Measurement Decoding Yusaku Yamada, Jun Suzuki (UEC) IT2022-106 ISEC2022-85 WBS2022-103 RCC2022-103 |
In this work, we study the problem of supervised label classification using quantum neural network (QNN). We propose a m... [more] | IT2022-106 ISEC2022-85 WBS2022-103 RCC2022-103 pp.242-247 |
| QIT (2nd) |
2022-12-08 18:15 |
Kanagawa | Keio Univ. (Primary: On-site, Secondary: Online) |
Quantum Fisher kernel for mitigating the vanishing similarity issue Yudai Suzuki, Hideaki Kawaguchi, Naoki Yamamoto (Keio Univ.) |
Quantum kernel method is a machine learning model exploiting quantum computers to calculate the quantum kernels (QKs) th... [more] | |
| QIT (2nd) |
2022-12-08 14:00 |
Kanagawa | Keio Univ. (Primary: On-site, Secondary: Online) |
[Poster Presentation]
Effect of entanglement on the trainability of over-parametrized quantum circuits Wolfgang Glaeser, Naoki Yamamoto (Keio Univ.) |
In this work, the effect of entanglement on the performance and trainability of parametrized quantum circuits (PQC) was ... [more] | |
| QIT (2nd) |
2022-05-31 13:50 |
Online | Online | Estimation of noiseless expectation value by quantum state tomography with generative model Ryo Maekura (Tokyo Univ.), Yasunari Suzuki (NTT), Nobuyuki Yoshioka (Tokyo Univ.), Yuki Tokunaga (NTT) |
To obtain reliable computational results with noisy quantum computers, we need to suppress computational errors stemming... [more] | |
| IBISML | 2022-01-17 11:00 |
Online | Online | Cluster approximation in quantum Boltzmann machine based on information geometry Masaya Hoshikawa, Tomohiro Ogawa (UEC) IBISML2021-21 |
A Boltzmann Machine (BM) is a model of machine learning which consists of mutually connected probabilistic binary units... [more] |
IBISML2021-21 pp.23-28 |
| QIT (2nd) |
2021-11-30 13:30 |
Online | Online | [Poster Presentation]
Machine Learning techniques for unitary design classification:A comparative study Yaswitha Gujju, Bo Yang, Dr. Yuko Kuroki, Dr. Hiroshi Imai (UTokyo) |
The recent use of correlator functions to identify the degree of pseudorandomness in a qubit system opens up immense pos... [more] | |
| QIT (2nd) |
2021-05-25 09:30 |
Online | Online | Acceleration of Extreme Learning Machines by Dequantization of Quantum Singular Value Decomposition Iori Takeda, Souichi Takahira, Kousuke Mitarai, Keisuke Fujii (Osaka Univ.) |
In 2016, the quantum recommendation system was proposed by Kerenidis and Prakash, and it was shown that the singular val... [more] | |
| QIT (2nd) |
2021-05-24 13:30 |
Online | Online | [Poster Presentation]
Machine Learning of Quantum Circuits Using OTOC Ryosuke Kutsuzawa, Naoya Kobayashi, Masahiro Fujii (Shizuoka Univ), Yoshifumi Nakata (Tokyo Univ), Yasunari Suzuki (NTT), Masaki Owari (Shizuoka Univ) |
Recently, Alves et al. succeeded a discrimination among Haar measure, 1-design, and 2-design by learning out of time ord... [more] | |
| MWP | 2019-11-27 15:20 |
Tokyo | Kikai-Shinko-Kaikan Bldg. | [Invited Talk]
Quantum Cascade Lasers for sensing applications Shinji Saito, Rei Hashimoto, Kei Kaneko, Tsutomu Kakuno (Toshiba Co.), Kazuaki Sakoda (NIMS) MWP2019-45 |
Quantum cascade lasers (QCLs) are laser devices of generating wide wavelength range from the mid-infrared to the THz ran... [more] | MWP2019-45 pp.13-16 |
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