arXiv subjects

Takahiro Yamamoto

Publications and source records attributed to Takahiro Yamamoto.

At least 19 recordsLinked to original sources

arXiv2026

Statistical-Uncertainty-Driven Selection of Evaluation Frequency for Time-Dependent Sensing Calibration: A Demonstration with KAGRA Data

Accurate calibration of the gravitational-wave strain h(t) is essential for both detection and astrophysical inference. In operating detectors, slow temporal variations in the sensing response are tracked using calibration lines, but practical constraints can prevent those lines from being injected at frequencies that are favorable for precise estimation of sensing-side parameters. We present a statistical framework for preselecting evaluation frequencies under such constraints. We apply this framework to KAGRA data from the first part of the fourth LIGO-Virgo-KAGRA Observing Run, for which the nominal cavity-pole frequency was about 18 Hz, while the sensing-side calibration line used in practice was injected at 32.7 Hz. For each candidate evaluation frequency, we construct the sensing function, quantify its segment-wise statistical uncertainty from empirical percentiles of the sample distribution, and rank the candidates using a score that combines the interval widths of the amplitude and phase. When a 1% amplitude interval width and a 1 degree phase interval width are weighted equally, 244 Hz is selected in all 4096 s analysis segments throughout the analyzed period. Relative to the reference frequency of 32.7 Hz, the amplitude interval width is reduced to about one quarter over a broad frequency range, while the phase interval width remains broadly comparable. We also assess the discrepancy introduced by frequency translation separately. These results suggest that the proposed method provides a useful statistical preselection framework for evaluation frequencies under practical operational constraints.

gr-qc↗

arXiv2026

Statistical Estimation and Correction of Model-Measurement Bias in Time-Dependent Correction Factors of KAGRA

Calibration of gravitational-wave detectors reconstructs the strain h(t) from the detector output, and bias and uncertainty in this reconstruction directly affect downstream analyses. In ground-based interferometers, time-dependent correction factors (TDCFs) are estimated from calibration lines to track temporal variations of the detector response, while the underlying model parameters are periodically updated using broadband swept-sine calibration measurements (SSCMs). However, if a model-measurement bias exists between the measured transfer function and the reference model, the TDCFs inferred from calibration lines can introduce a systematic deviation into the reconstructed strain. We propose a statistical framework to estimate and correct this bias using repeated measurement-to-model ratios at the calibration-line frequencies. The bias correction factors are estimated with a rolling random-effects model based on restricted maximum likelihood (REML) and incorporated into the TDCF estimation, with their uncertainty propagated to the reconstructed response. Applying the method to KAGRA O4c data, we find that the uncorrected response shows deviations of up to approximately 7% in magnitude and 5 degrees in phase relative to the SSCM-based reference in representative examples. The correction reduces these deviations, with a modest increase in the propagated uncertainty due to the included correction-factor uncertainty. This framework provides a practical way to combine broadband reference models with calibration-line-based tracking when model-measurement bias is present.

astro-ph.IM↗

arXiv2026

Thermal Einstein-de Haas Effect Induced by Chiral Phonons in Carbon Nanotubes

We investigate the effects of chirality on phonon thermal transport in semiconducting chiral single-walled carbon nanotubes (SWCNTs) using lattice dynamics combined with Boltzmann transport theory. We find that transverse acoustic and optical phonon modes, which are degenerate in nonchiral zigzag and armchair SWCNTs, are split in chiral SWCNTs, giving rise to finite phonon angular momentum associated with circular motion of individual atoms. This angular momentum is most efficiently generated in small-diameter nanotubes with intermediate chiral angles. Consequently, chiral SWCNTs are predicted to undergo thermally induced rigid-body rotation with an experimentally observable angular velocity via the thermal Einstein-de Haas effect.

cond-mat.mes-hall↗

arXiv2025

Frequency Dependence of Phonon-Induced Current Noise in ArmchairCarbon Nanotube

We theoretically investigate the frequency dependence of phonon-induced current noise in armchair carbon nanotubes at room temperature. Our results reveal the emergence of multiple resonance peaks in the high-frequency regime, which cannot be accounted for by the Lorentzian lineshape expected from a Markovian process. The electron-phonon scattering processes responsible for most of these peaks are identified based on energy and momentum conservation laws and conventional selection rules. However, certain peaks cannot be fully explained within the framework of harmonic phonon scattering, suggesting the involvement of nontrivial interactions between electrons and anharmonic phonons.

cond-mat.mes-hall↗

arXiv2025

Statistical Properties of Current Noise Induced by Electron-Phonon Scattering in Metallic Carbon Nanotubes

We theoretically investigate current noise in metallic carbon nanotubes induced by electron-phonon scattering, focusing on the probability density function (PDF) of the current that characterizes the nonequilibrium steady state. Quantum transport simulations combined with analyses of higher-order statistical moments reveal that the PDF evolves continuously from a Gaussian distribution in the ballistic regime to a non-Gaussian gamma distribution in the diffusive regime. In the crossover regime, the PDF exhibits pronounced asymmetry, attributed to a statistical imbalance in the number of conduction pathways contributing to high- and low-current events. Furthermore, in the diffusive regime, we identify non-Markovian features arising from high-frequency resonances in the current noise, which dominate the asymptotic scaling behavior of the current variance.

cond-mat.mes-hall↗

arXiv2025

Phonon-Induced Current Noise in Single-Walled Carbon Nanotubes across the Ballistic-Diffusive Crossover

We theoretically elucidate the system length ($L$) dependence of phonon-induced current noise in carbon nanotubes at room temperature over a broad range, encompassing the quantum ballistic and classical diffusive regimes. The power spectral density for the current noise is maximally enhanced when $L$ is comparable to the mean free path $L_0$ of an electron. In the ballistic limit of $L/L_0\ll 1$, the power spectral density increases in proportion to $L$, whereas in the diffusive limit of $L/L_0\gg 1$, it shows a power-law decay $L^{-α}$ with a scaling parameter $α=3.81$. The noise decay for single-walled carbon nanotubes is faster than that previously predicted based on a simple model because of the various electron-phonon scattering processes and the complex energy dependence of the phonon relaxation time.

cond-mat.mes-hall↗

arXiv2025

Photon Calibration Performance of KAGRA during the 4th Joint Observing Run (O4)

KAGRA is a kilometer-scale cryogenic gravitational-wave (GW) detector in Japan. It joined the 4th joint observing run (O4) in May 2023 in collaboration with the Laser Interferometer GW Observatory (LIGO) in the USA, and Virgo in Italy. After one month of observations, KAGRA entered a break period to enhance its sensitivity to GWs, and it is planned to rejoin O4 before its scheduled end in October 2025. To accurately recover the information encoded in the GW signals, it is essential to properly calibrate the observed signals. We employ a photon calibration (Pcal) system as a reference signal injector to calibrate the output signals obtained from the telescope. In ideal future conditions, the uncertainty in Pcal could dominate the uncertainty in the observed data. In this paper, we present the methods used to estimate the uncertainty in the Pcal systems employed during KAGRA O4 and report an estimated system uncertainty of 0.79%, which is three times lower than the uncertainty achieved in the previous 3rd joint observing run (O3) in 2020. Additionally, we investigate the uncertainty in the Pcal laser power sensors, which had the highest impact on the Pcal uncertainty, and estimate the beam positions on the KAGRA main mirror, which had the second highest impact. The Pcal systems in KAGRA are the first fully functional calibration systems for a cryogenic GW telescope. To avoid interference with the KAGRA cryogenic systems, the Pcal systems incorporate unique features regarding their placement and the use of telephoto cameras, which can capture images of the mirror surface at almost normal incidence. As future GW telescopes, such as the Einstein Telescope, are expected to adopt cryogenic techniques, the performance of the KAGRA Pcal systems can serve as a valuable reference.

astro-ph.IM↗

arXiv2025

Sommerfeld-Bethe analysis of ZT in inhomogeneous thermoelectrics

The development of good thermoelectric materials exhibiting high $ZT$ (=$\frac{PF}κ T$) requires maximizing power factor, $PF$, mainly governed by electrons, and minimizing thermal conductivity, $κ$, associated not only with electrons but also with phonons. In the present work, we focus on the GeTe and Mg$_3$Sb$_2$ as high $ZT$ materials with inhomogeneous structures and analyze both electrical conductivity, $L_{11}$, and Seebeck coefficient, $S$, with help of Sommerfeld-Bethe formula, resulting in understanding the temperature dependence of $PF$ and the identification of electrons contribution to thermal conductivity, $κ_{\rm el}$. Comparing the obtained $κ_{\rm el}$ and experimentally measured $κ$, the temperature dependence of phonons contribution to thermal conductivity, $κ_{\rm ph}=κ-κ_{\rm el}$, is inferred and analyzed based on the formula by Holland. Comparison of the GeTe and Mg$_3$Sb$_2$ with different types of crystal structures, i.e., GeTe being of a semiordered zigzag nanostructure like a disrupted herringbone structure while Mg$_3$Sb$_2$ of rather uniform amorphous structure, discloses that size effects on temperature dependence of $κ_{\rm ph}$ is large in the former, while very small in the latter. Hence, it is concluded that not only the size of the grain but also its shape has an important influence on $κ_{\rm ph}$ and then $ZT$.

cond-mat.mtrl-sci↗

arXiv2024

Development of Low-Cost IoT Units for Thermal Comfort Measurement and AC Energy Consumption Prediction System

In response to the substantial energy consumption in buildings, the Japanese government initiated the BI-Tech (Behavioral Insights X Technology) project in 2019, aimed at promoting voluntary energy-saving behaviors through the utilization of AI and IoT technologies. Our study aimed at small and medium-sized office buildings introduces a cost-effective IoT-based BI-Tech system, utilizing the Raspberry Pi 4B+ platform for real-time monitoring of indoor thermal conditions and air conditioner (AC) set-point temperature. Employing machine learning and image recognition, the system analyzes data to calculate the PMV index and predict energy consumption changes due to temperature adjustments. The integration of mobile and desktop applications conveys this information to users, encouraging energy-efficient behavior modifications. The machine learning model achieved with an R2 value of 97%, demonstrating the system's efficiency in promoting energy-saving habits among users.

cs.LG↗

arXiv2024

Scaling theory of charge transport and thermoelectric response in disordered 2D electron systems: From weak to strong localization

We develop a new theoretical scheme for charge transport and thermoelectric response in two-dimensional disordered systems exhibiting crossover from weak localization (WL) to strong localization (SL). The scheme is based on the scaling theory for Anderson localization combined with the Kubo-Luttinger theory. Key aspects of the scheme include introducing a unified $β$ function that seamlessly connects the WL and SL regimes, as well as describing the temperature ($T$) dependence of the conductance from high to low $T$ regions on the basis of the dephasing length. We found that the Seebeck coefficient, $S$, behaves as $S\propto T$ in the WL limit and as $S\propto T^{1-p}$ ($p < 1$) in the SL limit, both with possible logarithmic corrections. The scheme is applied to analyze experimental data for thin films of the p-type organic semiconductor poly[2,5-bis(3-alkylthiophen-2-yl)thieno(3,2-b)thiophene] (PBTTT).

cond-mat.mes-hall↗

arXiv2024

mRNA secondary structure prediction using utility-scale quantum computers

Recent advancements in quantum computing have opened new avenues for tackling long-standing complex combinatorial optimization problems that are intractable for classical computers. Predicting secondary structure of mRNA is one such notoriously difficult problem that can benefit from the ever-increasing maturity of quantum computing technology. Accurate prediction of mRNA secondary structure is critical in designing RNA-based therapeutics as it dictates various steps of an mRNA life cycle, including transcription, translation, and decay. The current generation of quantum computers have reached utility-scale, allowing us to explore relatively large problem sizes. In this paper, we examine the feasibility of solving mRNA secondary structures on a quantum computer with sequence length up to 60 nucleotides representing problems in the qubit range of 10 to 80. We use Conditional Value at Risk (CVaR)-based VQE algorithm to solve the optimization problems, originating from the mRNA structure prediction problem, on the IBM Eagle and Heron quantum processors. To our encouragement, even with ``minimal'' error mitigation and fixed-depth circuits, our hardware runs yield accurate predictions of minimum free energy (MFE) structures that match the results of the classical solver CPLEX. Our results provide sufficient evidence for the viability of solving mRNA structure prediction problems on a quantum computer and motivate continued research in this direction.

quant-ph↗

arXiv2024

Extension of the characterization method for non-Gaussianity in gravitational wave detector with statistical hypothesis test

In gravitational wave astronomy, non-Gaussian noise, such as scattered light noise disturbs stable interferometer operation, limiting the interferometer's sensitivity, and reducing the reliability of the analyses. In scattered light noise, the non-Gaussian noise dominates the sensitivity in a low frequency range of less than a few hundred Hz, which is sensitive to gravitational waves from compact binary coalescence. This non-Gaussian noise prevents reliable parameter estimation, since several analysis methods are optimized only for Gaussian noise. Therefore, identifying data contaminated by non-Gaussian noise is important. In this work, we extended the conventional method to evaluate non-Gaussian noise, Rayleigh statistic, by using a statistical hypothesis test to determine a threshold for non-Gaussian noise. First, we estimated the distribution of the Rayleigh statistic against Gaussian noise, called the background distribution, and validated that our extension serves as the hypothetical test. Moreover, we investigated the detection efficiency by assuming two non-Gaussian noise models. For example, for the model with strong scattered light noise, the true positive rate was always above 0.7 when the significance level was 0.05. The results showed that our extension can contribute to an initial detection of non-Gaussian noise and lead to further investigation of the origin of the non-Gaussian noise.

gr-qc↗

arXiv2024

Explicitly Multi-Modal Benchmarks for Multi-Objective Optimization

In multi-objective optimization, designing good benchmark problems is an important issue for improving solvers. Controlling the global location of Pareto optima in existing benchmark problems has been problematic, and it is even more difficult when the design space is high-dimensional since visualization is extremely challenging. As a benchmarking with explicit local Pareto fronts, we introduce a benchmarking based on basin connectivity (3BC) by using basins of attraction. The 3BC allows for the specification of a multimodal landscape through a kind of topological analysis called the basin graph, effectively generating optimization problems from this graph. Various known indicators measure the performance of a solver in searching global Pareto optima, but using 3BC can make us localize them for each local Pareto front by restricting it to its basin. 3BC's mathematical formulation ensures the accurate representation of the specified optimization landscape, guaranteeing the existence of intended local and global Pareto optima.

math.OC↗

arXiv2023

Quantum Multiple Kernel Learning in Financial Classification Tasks

Financial services is a prospect industry where unlocked near-term quantum utility could yield profitable potential, and, in particular, quantum machine learning algorithms could potentially benefit businesses by improving the quality of predictive models. Quantum kernel methods have demonstrated success in financial, binary classification tasks, like fraud detection, and avoid issues found in variational quantum machine learning approaches. However, choosing a suitable quantum kernel for a classical dataset remains a challenge. We propose a hybrid, quantum multiple kernel learning (QMKL) methodology that can improve classification quality over a single kernel approach. We test the robustness of QMKL on several financially relevant datasets using both fidelity and projected quantum kernel approaches. We further demonstrate QMKL on quantum hardware using an error mitigation pipeline and show the benefits of QMKL in the large qubit regime.

quant-ph↗

arXiv2023

Two-band Model with High Thermoelectric Power Factor and Its Application to FeSe Thin Film

We propose a simple theoretical model referred to as the {\it two-band model} to realize both a large Seebeck coefficient and high electrical conductivity, resulting in a high thermoelectric (TE) power factor ($PF$). Using the Kubo--Luttinger linear response theory, we apply this model to the TE response of an FeSe thin film reported by Shimizu {\it et al.} to show a high $PF$ with strong temperature dependence. The effects of superconducting fluctuations and excitonic correlations are found to not be critical.

cond-mat.mtrl-sci↗

arXiv2022

Error suppression by a virtual two-qubit gate

Sparse connectivity of a superconducting quantum computer results in the large experimental overheads of SWAP gates. In this study, we consider employing a virtual two-qubit gate (VTQG) as an error suppression technique. The VTQG enables a non-local operation between a pair of distant qubits using only single qubit gates and projective measurements. Here, we apply the VTQG to the digital quantum simulation of the transverse-field Ising model on an IBM quantum computer to suppress the errors due to the noisy two-qubit operations. We present an effective use of VTQG, where the reduction of multiple SWAP gates results in increasing the fidelity of the output states. The obtained results indicate that the VTQG can be useful for suppressing the errors due to the additional SWAP gates. Additionally, by combining a pulse-efficient transpilation method with the VTQG, further suppression of the errors is observed. In our experiments, we have observed one order of magnitude improvement in accuracy for the quantum simulation of the transverse-field Ising model with 8 qubits.

quant-ph↗

arXiv2022

Training Process of Unsupervised Learning Architecture for Gravity Spy Dataset

Transient noise appearing in the data from gravitational-wave detectors frequently causes problems, such as instability of the detectors and overlapping or mimicking gravitational-wave signals. Because transient noise is considered to be associated with the environment and instrument, its classification would help to understand its origin and improve the detector's performance. In a previous study, an architecture for classifying transient noise using a time-frequency 2D image (spectrogram) is proposed, which uses unsupervised deep learning combined with variational autoencoder and invariant information clustering. The proposed unsupervised-learning architecture is applied to the Gravity Spy dataset, which consists of Advanced Laser Interferometer Gravitational-Wave Observatory (Advanced LIGO) transient noises with their associated metadata to discuss the potential for online or offline data analysis. In this study, focused on the Gravity Spy dataset, the training process of unsupervised-learning architecture of the previous study is examined and reported.

gr-qc↗

arXiv2022

A Gauss-Newton based Quantum Algorithm for Combinatorial Optimization

In this work, we present a Gauss-Newton based quantum algorithm (GNQA) for combinatorial optimization problems that, under optimal conditions, rapidly converges towards one of the optimal solutions without being trapped in local minima or plateaus. Quantum optimization algorithms have been explored for decades, but more recent investigations have been on variational quantum algorithms, which often suffer from the aforementioned problems. Our approach mitigates those by employing a tensor product state that accurately represents the optimal solution, and an appropriate function for the Hamiltonian, containing all the combinations of binary variables. Numerical experiments presented here demonstrate the effectiveness of our approach, and they show that GNQA outperforms other optimization methods in both convergence properties and accuracy for all problems considered here. Finally, we briefly discuss the potential impact of the approach to other problems, including those in quantum chemistry and higher order binary optimization.

quant-ph↗