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Ph.D. candidate in Information and Communication Engineering
Institute of Science Tokyo
4259 Nagatsuta-cho, Midori-ku
Yokohama, Kanagawa 226-8503, Japan
Ludwig Boltzmann, who spent much of his life studying statistical mechanics, died in 1906, by his own hand. Paul Ehrenfest, carrying on the work, died similarly in 1933. Now it is our turn to study statistical mechanics. Perhaps it will be wise to approach the subject cautiously.
— David L. Goodstein, States of Matter
I am a Ph.D. candidate at the Institute of Science Tokyo (formerly Tokyo Institute of Technology), where I am supervised by Prof. Konstantinos Slavakis. My research focuses on the intersection of quantum computing and machine learning.
I obtained my B.S. in Physics from the University of California, San Diego and my M.S. in Experimental Particle Physics from KEK.
Scientific computing. LogosQ is a quantum computing library written in Rust, focused on efficient circuit simulation and optimization. Tutorials and documentation are at logosqbook.vercel.app. ManifoldsGPU.jl brings GPU and CUDA acceleration to the JuliaManifolds ecosystem, so that Riemannian optimization on products of unitary and Stiefel manifolds runs on the accelerator instead of falling back to the CPU. It grew out of the training needs of pdft. Both projects are open source, and contributions are welcome.
Quantum machine learning. I work on learnable Fourier bases and parameterized tensor networks. Viewed as a circuit of one- and two-qubit gates, the quantum Fourier transform becomes a family of bases that can be trained on unitary manifolds while keeping exact invertibility and near-linear transform cost. Trained this way, pdft compresses images better than the fixed DFT or DCT (arXiv:2608.00053), and with the Hadamards held fixed the same circuits inpaint images with minimum coherence guaranteed by construction (arXiv:2609.17298). Earlier work proposed a tensor-based binary graph encoding for variational quantum classifiers.
| Sep 2026 | "Quantum-Inspired Trainable and Parameter-Efficient Tensor Networks for Image Inpainting" got published on arXiv. |
| Aug 2026 | "Fast Trainable Multilinear Bases for Image Compression" got published on arXiv. |
| Jun 2026 | "Riemannian Gauss–Newton Method for Open-System Projected Variational Quantum Dynamics" is accepted at IEEE QCE 2026 in Toronto, Canada. |
| Apr 2026 | "Problem Reductions at Scale: Agentic Integration of Computationally Hard Problems" got published on arXiv. |
| Jan 2026 | Visiting scholar at HKUST Guangzhou from January to March 2026. |
| Dec 2025 | The official paper on the LogosQ is published on Arxiv |
| Apr 2025 | The paper on graph encoding with variational quantum circuit is accepted by IEEE QCNC 2025 in Nara, Japan. |
| May 2024 | "Search for Nearly Mass-Degenerate Higgsinos Using Low-Momentum Mildly Displaced Tracks in pp Collisions at √s = 13 TeV with the ATLAS Detector" is published in Physical Review Letters. |
| Apr 2024 | "Combination of searches for heavy spin-1 resonances using 139 fb−1 of proton-proton collision data at √s = 13 TeV with the ATLAS detector" is published in JHEP. |
| Sep 2023 | Finished the M.Sc. at KEK on the ATLAS experiment and started the Ph.D. at Tokyo Institute of Technology. |
| Mar 2023 | "Background Estimation for Non-resonant Di-Higgs Production Search in the bbtautau Final State on the ATLAS experiment" is presented at the JPS 2023 Spring Meeting. |
| Oct 2022 | "Overnight Stock Movement Prediction with Contextualized Embedding Incorporating News and Stock" appears in the proceedings of IEEE GCCE 2022 in Osaka, Japan. |
| Mar 2022 | "Performance Evaluation of Quality Control Data Acquisition System for Silicon Pixel Detector Production in ATLAS Experiment High-Luminosity LHC Upgrade" is presented at the JPS 77th Annual Meeting. |
Details of each item are on the news page.
Last updated November 1, 2026.