Zobrazeno 1 - 10
of 5 459
pro vyhledávání: '"Nakaji, So"'
Out-of-distribution Reject Option Method for Dataset Shift Problem in Early Disease Onset Prediction
Autor:
Tosaki, Taisei, Uchino, Eiichiro, Kojima, Ryosuke, Mineharu, Yohei, Arita, Mikio, Miyai, Nobuyuki, Tamada, Yoshinori, Mikami, Tatsuya, Murashita, Koichi, Nakaji, Shigeyuki, Okuno, Yasushi
Machine learning is increasingly used to predict lifestyle-related disease onset using health and medical data. However, the prediction effectiveness is hindered by dataset shift, which involves discrepancies in data distribution between the training
Externí odkaz:
http://arxiv.org/abs/2405.19864
Autor:
Guo, Naixu, Yu, Zhan, Choi, Matthew, Agrawal, Aman, Nakaji, Kouhei, Aspuru-Guzik, Alán, Rebentrost, Patrick
Generative machine learning methods such as large-language models are revolutionizing the creation of text and images. While these models are powerful they also harness a large amount of computational resources. The transformer is a key component in
Externí odkaz:
http://arxiv.org/abs/2402.16714
Autor:
Wang, Lujia, Wang, Hairong, D'Angelo, Fulvio, Curtin, Lee, Sereduk, Christopher P., De Leon, Gustavo, Singleton, Kyle W., Urcuyo, Javier, Hawkins-Daarud, Andrea, Jackson, Pamela R., Krishna, Chandan, Zimmerman, Richard S., Patra, Devi P., Bendok, Bernard R., Smith, Kris A., Nakaji, Peter, Donev, Kliment, Baxter, Leslie C., Mrugała, Maciej M., Ceccarelli, Michele, Iavarone, Antonio, Swanson, Kristin R., Tran, Nhan L., Hu, Leland S., Li, Jing
Glioblastoma (GBM) is one of the most aggressive and lethal human cancers. Intra-tumoral genetic heterogeneity poses a significant challenge for treatment. Biopsy is invasive, which motivates the development of non-invasive, MRI-based machine learnin
Externí odkaz:
http://arxiv.org/abs/2401.00128
Partial differential equations (PDEs) are ubiquitous in science and engineering. Prior quantum algorithms for solving the system of linear algebraic equations obtained from discretizing a PDE have a computational complexity that scales at least linea
Externí odkaz:
http://arxiv.org/abs/2306.11802
Estimation of the expectation value of observables is a key subroutine in quantum computing and is also the bottleneck of the performance of many near-term quantum algorithms. Many works have been proposed to reduce the number of measurements needed
Externí odkaz:
http://arxiv.org/abs/2305.02439
Publikováno v:
Quantum Mach. Intell. 6, 14 (2024)
Quantum machine learning with variational quantum algorithms (VQA) has been actively investigated as a practical algorithm in the noisy intermediate-scale quantum (NISQ) era. Recent researches reveal that the data reuploading, which repeatedly encode
Externí odkaz:
http://arxiv.org/abs/2305.00688
Publikováno v:
PRX Quantum 5, 020330 (2024)
Hamiltonian simulation is known to be one of the fundamental building blocks of a variety of quantum algorithms such as its most immediate application, that of simulating many-body systems to extract their physical properties. In this work, we presen
Externí odkaz:
http://arxiv.org/abs/2302.14811
Approximate complex amplitude encoding algorithm and its application to data classification problems
Autor:
Mitsuda, Naoki, Ichimura, Tatsuhiro, Nakaji, Kouhei, Suzuki, Yohichi, Tanaka, Tomoki, Raymond, Rudy, Tezuka, Hiroyuki, Onodera, Tamiya, Yamamoto, Naoki
Publikováno v:
Phys. Rev. A 109, 052423 (2024)
Quantum computing has a potential to accelerate the data processing efficiency, especially in machine learning, by exploiting special features such as the quantum interference. The major challenge in this application is that, in general, the task of
Externí odkaz:
http://arxiv.org/abs/2211.13039
Quantum machine learning (QML) is the spearhead of quantum computer applications. In particular, quantum neural networks (QNN) are actively studied as the method that works both in near-term quantum computers and fault-tolerant quantum computers. Rec
Externí odkaz:
http://arxiv.org/abs/2209.01958
Publikováno v:
Quantum 7, 995 (2023)
Simulating large quantum systems is the ultimate goal of quantum computing. Variational quantum simulation (VQS) gives us a tool to achieve the goal in near-term devices by distributing the computation load to both classical and quantum computers. Ho
Externí odkaz:
http://arxiv.org/abs/2208.13934