Search Results for author: Naoki Yamamoto

Found 10 papers, 1 papers with code

Quantum Inception Score

no code implementations20 Nov 2023 Akira Sone, Akira Tanji, Naoki Yamamoto

Motivated by the great success of classical generative models in machine learning, enthusiastic exploration of their quantum version has recently started.

Quantum Noise-Induced Reservoir Computing

no code implementations16 Jul 2022 Tomoyuki Kubota, Yudai Suzuki, Shumpei Kobayashi, Quoc Hoan Tran, Naoki Yamamoto, Kohei Nakajima

We demonstrate this ability in several typical benchmarks and investigate the information processing capacity to clarify the framework's processing mechanism and memory profile.

Overfitting in quantum machine learning and entangling dropout

no code implementations23 May 2022 Masahiro Kobayashi, Kouhei Nakaji, Naoki Yamamoto

The ultimate goal in machine learning is to construct a model function that has a generalization capability for unseen dataset, based on given training dataset.

BIG-bench Machine Learning Quantum Machine Learning

Pulse-engineered Controlled-V gate and its applications on superconducting quantum device

no code implementations11 Feb 2021 Shun Oomura, Takahiko Satoh, Michihiko Sugawara, Naoki Yamamoto

In this paper, we demonstrate that, by employing OpenPulse design kit for IBM superconducting quantum devices, the controlled-V gate (CV gate) can be implemented in about half the gate time to the controlled-X (CX or CNOT gate) and consequently 65. 5\% reduced gate time compared to the CX-based implementation of CV.

Quantum Physics

Wigner functions and quantum kinetic theory of polarized photons

no code implementations26 Oct 2020 Koichi Hattori, Yoshimasa Hidaka, Naoki Yamamoto, Di-Lun Yang

Moreover, using the real-time formalism, we construct the quantum kinetic theory (QKT) for polarized photons.

High Energy Physics - Phenomenology High Energy Astrophysical Phenomena Other Condensed Matter High Energy Physics - Theory Nuclear Theory

Quantum self-learning Monte Carlo with quantum Fourier transform sampler

1 code implementation28 May 2020 Katsuhiro Endo, Taichi Nakamura, Keisuke Fujii, Naoki Yamamoto

The self-learning Metropolis-Hastings algorithm is a powerful Monte Carlo method that, with the help of machine learning, adaptively generates an easy-to-sample probability distribution for approximating a given hard-to-sample distribution.

Quantum Physics

Temporal Information Processing on Noisy Quantum Computers

no code implementations26 Jan 2020 Jiayin Chen, Hendra I. Nurdin, Naoki Yamamoto

The combination of machine learning and quantum computing has emerged as a promising approach for addressing previously untenable problems.

speech-recognition Speech Recognition

Quantum functionalities via feedback amplification

no code implementations27 Sep 2019 Rion Shimazu, Naoki Yamamoto

Feedback amplification is a key technique for synthesizing various important functionalities, especially in electronic circuits involving op-amps.

Quantum Physics

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