arXiv subjects

Seiji Kajita

Publications and source records attributed to Seiji Kajita.

15 recordsLinked to original sources

arXiv2025

Velocity dependence of kinetic friction by multi-scale Quantum Mechanics/Green's Function molecular dynamics

Atomistic simulations are powerful tools for investigating tribological phenomena at a fundamental level; however, simulating a tribological system remains challenging due to the multiscale nature of frictional processes. Recently, we introduced a hybrid method, QM-GF, that enables an accurate description of both interfacial chemistry and phononic dissipation in semi-infinite bulks. In this work, we apply this simulation scheme to study the dependence of kinetic friction on sliding velocity. Using a prototypical diamond interface with varying hydrogen coverages, we find that the friction force decreases with increasing sliding velocity, revealing two distinct sliding regimes at low and high speeds. We provide a physical interpretation of this velocity dependence based on the modulation of the frictional force by the sliding motion over the periodic potential energy surface of the interface. High velocities lead to force cancellation, while low velocities result in a net frictional force characterized by a distinctive sawtooth profile.

cond-mat.mtrl-sci↗

arXiv2025

Model-based framework for automated quantification of error sources in quantum state tomography

High-quality quantum state generation is essential for advanced quantum information processing, including quantum communication, quantum sensing, and quantum computing. In practice, various error sources degrade the quality of quantum states, and quantum state tomography (QST) is a standard diagnostic tool. However, in QST, multiple error sources gather in a single density matrix, making it difficult to identify individual error sources. To address this problem, we propose an automated method for quantifying error sources by combining simulation and parameter optimization to reproduce the experimental density matrix. We focus on the experimental generation of time-bin entangled photon pairs, for which we model the relevant error sources and simulate the density matrix with adjustable model parameters, thereby optimizing the parameters and minimizing the trace distance to the experimental data. Optimization of the parameters reduced the trace distance from 0.177 to 0.024, indicating that our modeled error sources explain 86% of the errors. Reducing the predicted error sources improves the state quality, consistent with our predictions and thus validating the proposed method. In addition, the modular structure of this framework makes it applicable to other quantum platforms, such as superconducting qubits, atoms, and solid-state spins.

quant-ph↗

arXiv2025

Fast spatio-temporally varying coefficient modeling with reluctant interaction selection

Spatially and temporally varying coefficient (STVC) models are currently attracting attention as a flexible tool to explore the spatio-temporal patterns in regression coefficients. However, these models often struggle with balancing computational efficiency and model flexibility. To address this challenge, this study develops a fast and flexible method for STVC modeling. For enhanced flexibility in modeling, we assume multiple processes in each varying coefficient, including purely spatial, purely temporal, and spatio-temporal interaction processes with or without time cyclicity. While considering multiple processes can be time consuming, we combine a pre-conditioning method with a model selection procedure, inspired by reluctant interaction modeling. This approach allows us to computationally efficiently select and specify the latent space-time structure. Monte Carlo experiments demonstrate that the proposed method outperforms alternatives in terms of coefficient estimation accuracy and computational efficiency. Finally, we apply the proposed method to crime analysis using a sample size of 279,360, confirming that the proposed method provides reasonable estimates of varying coefficients. The STVC model is implemented in an R package spmoran.

stat.ME↗

arXiv2024

Fast simulation for multi-photon, atomic-ensemble quantum model of linear optical systems addressing the curse of dimensionality

Photons are elementary particles of light in quantum mechanics, whose dynamics can be difficult to gain detailed insights, especially in complex systems. Simulation is a promising tool to resolve this issue, but it must address the curse of dimensionality, namely, that the number of bases increases exponentially in the number of photons. Here we mitigate this dimensionality scaling by focusing on optical systems composed of linear optical objects, modeled as an ensemble of two-level atoms. We decompose the time evolutionary operator on multiple photons into a group of time evolution operators acting on a single photon. Since the dimension of a single-photon time evolution operator is exponentially smaller than that of a multi-photon one in the number of photons, the decomposition enables the multi-photon simulations to be performed at a much lower computational cost. We apply this method to basic single- and multi-photon phenomena, such as Hong-Ou-Mandel interference and violation of the Bell-CHSH inequality, and confirm that the calculated properties are quantitatively comparable to the experimental results. Furthermore, our method visualizes the spatial propagation of photons hence provides insights that aid experiment designs for quantum-enabled technologies.

quant-ph↗

arXiv2023

Creation of crystal structure reproducing X-ray diffraction pattern without using database

When a sample's X-ray diffraction pattern (XRD) is measured, the corresponding crystal structure is usually determined by searching for similar XRD patterns in the database. However, if a similar XRD pattern is not found, it is tremendously laborious to identify the crystal structure even for experts. This case commonly happens when researchers develop novel and complex materials. In this study, we propose a crystal structure creation scheme that reproduces a given XRD pattern. We employed a combinatorial inverse design method using an evolutionary algorithm and crystal morphing (Evolv&Morph) supported by Bayesian optimization, which maximizes the similarity of the XRD patterns between target one and those of the created crystal structures. For sixteen different crystal structure systems with twelve simulated and four powder target XRD patterns, Evolv&Morph successfully created crystal structures with the same XRD pattern as the target (cosine similarity > 99% for the simulated ones and > 96% the experimentally-measured ones). Furthermore, the present method has merits in that it is an automated crystal structure creation scheme, not dependent on a database. We believe that Evolv&Morph can be applied not only to determine crystal structures but also to design materials for specific properties.

cond-mat.mtrl-sci↗

arXiv2023

Accurate multiscale simulation of frictional interfaces by Quantum Mechanics/Green's Function molecular dynamics

Understanding frictional phenomena is a fascinating fundamental problem with huge potential impact on energy saving. Such an understanding requires monitoring what happens at the sliding buried interface, which is almost inaccessible by experiments. Simulations represent powerful tools in this context, yet a methodological step forward is needed to fully capture the multiscale nature of the frictional phenomena. Here, we present a multiscale approach based on linked ab initio and Green's function molecular dynamics, which is above the state-of-the-art techniques used in computational tribology as it allows for a realistic description of both the interfacial chemistry and energy dissipation due to bulk phonons in non-equilibrium conditions. By considering a technologically relevant system composed of two diamond surfaces with different degrees of passivation, we show that the presented method can be used not only for monitoring in real-time tribolochemical phenomena such as the tribologically-induced surface graphitization and passivation effects but also for estimating realistic friction coefficients. This opens the way to in silico experiments of tribology to test materials to reduce friction prior to that in real labs.

cond-mat.mes-hall↗

arXiv2022

Spatial regression-based transfer learning for prediction problems

Although spatial prediction is widely used for urban and environmental monitoring, its accuracy is often unsatisfactory if only a small number of samples are available in the study area. The objective of this study was to improve the prediction accuracy in such a case through transfer learning using larger samples obtained outside the study area. Our proposal is to pre-train latent spatial-dependent processes, which are difficult to transfer, and apply them as additional features in the subsequent transfer learning. The proposed method is designed to involve local spatial dependence and can be implemented easily. This spatial-regression-based transfer learning is expected to achieve a higher and more stable prediction accuracy than conventional learning, which does not explicitly consider local spatial dependence. The performance of the proposed method was examined using land price and crime predictions. These results suggest that the proposed method successfully improved the accuracy and stability of these spatial predictions.

stat.AP↗

arXiv2022

Quantum topology optimization of ground structures using noisy intermediate-scale quantum devices

To arrive at some viable product design, product development processes frequently use numerical simulations and mathematical programming techniques. Topology optimization, in particular, is one of the most promising techniques for generating insightful design choices. Topology optimization problems reduce to an NP-hard combinatorial optimization problem, where the combination of the existence or absence of the material at some positions is optimized. In this study, we examine the usage of quantum computers as a potential solution to topology optimization problems. The proposed method consists of two variational quantum algorithms (VQAs): the first solves the state equilibrium equation for all conceivable material configurations, while the second amplifies the likelihood of an optimal configuration in quantum superposition using the first VQA's quantum state. Several experiments, including a real device experiment, show that the proposed method successfully obtained the optimal configurations. These findings suggest that quantum computers could be a potential tool for solving topology optimization problems and they open the window to the near-future product designs.

quant-ph↗

arXiv2022

Computationally Efficient Quantum Expectation with Extended Bell Measurements

Evaluating an expectation value of an arbitrary observable $A\in{\mathbb C}^{2^n\times 2^n}$ through naïve Pauli measurements requires a large number of terms to be evaluated. We approach this issue using a method based on Bell measurement, which we refer to as the extended Bell measurement method. This analytical method quickly assembles the $4^n$ matrix elements into at most $2^{n+1}$ groups for simultaneous measurements in $O(nd)$ time, where $d$ is the number of non-zero elements of $A$. The number of groups is particularly small when $A$ is a band matrix. When the bandwidth of $A$ is $k=O(n^c)$, the number of groups for simultaneous measurement reduces to $O(n^{c+1})$. In addition, when non-zero elements densely fill the band, the variance is $O((n^{c+1}/2^n)\,{\rm tr}(A^2))$, which is small compared with the variances of existing methods. The proposed method requires a few additional gates for each measurement, namely one Hadamard gate, one phase gate and at most $n-1$ CNOT gates. Experimental results on an IBM-Q system show the computational efficiency and scalability of the proposed scheme, compared with existing state-of-the-art approaches. Code is available at https://github.com/ToyotaCRDL/extended-bell-measurements.

quant-ph↗

arXiv2021

Compositionally-warped additive mixed modeling for a wide variety of non-Gaussian spatial data

As with the advancement of geographical information systems, non-Gaussian spatial data sets are getting larger and more diverse. This study develops a general framework for fast and flexible non-Gaussian regression, especially for spatial/spatiotemporal modeling. The developed model, termed the compositionally-warped additive mixed model (CAMM), combines an additive mixed model (AMM) and the compositionally-warped Gaussian process to model a wide variety of non-Gaussian continuous data including spatial and other effects. A specific advantage of the proposed CAMM is that it requires no explicit assumption of data distribution unlike existing AMMs. Monte Carlo experiments show the estimation accuracy and computational efficiency of CAMM for modeling non-Gaussian data including fat-tailed and/or skewed distributions. Finally, the model is applied to crime data to examine the empirical performance of the regression analysis and prediction. The result shows that CAMM provides intuitively reasonable coefficient estimates and outperforms AMM in terms of prediction accuracy. CAMM is verified to be a fast and flexible model that potentially covers a wide variety of non-Gaussian data modeling. The proposed approach is implemented in an R package spmoran.

stat.ME↗

arXiv2020

Scalable model selection for spatial additive mixed modeling: application to crime analysis

A rapid growth in spatial open datasets has led to a huge demand for regression approaches accommodating spatial and non-spatial effects in big data. Regression model selection is particularly important to stably estimate flexible regression models. However, conventional methods can be slow for large samples. Hence, we develop a fast and practical model-selection approach for spatial regression models, focusing on the selection of coefficient types that include constant, spatially varying, and non-spatially varying coefficients. A pre-processing approach, which replaces data matrices with small inner products through dimension reduction dramatically accelerates the computation speed of model selection. Numerical experiments show that our approach selects the model accurately and computationally efficiently, highlighting the importance of model selection in the spatial regression context. Then, the present approach is applied to open data to investigate local factors affecting crime in Japan. The results suggest that our approach is useful not only for selecting factors influencing crime risk but also for predicting crime events. This scalable model selection will be key to appropriately specifying flexible and large-scale spatial regression models in the era of big data. The developed model selection approach was implemented in the R package spmoran.

stat.AP↗

arXiv2019

Crime Prediction by Data-Driven Green's Function method

We develop an algorithm that forecasts cascading events, by employing a Green's function scheme on the basis of the self-exciting point process model. This method is applied to open data of 10 types of crimes happened in Chicago. It shows a good prediction accuracy superior to or comparable to the standard methods which are the expectation-maximization method and prospective hotspot maps method. We find a cascade influence of the crimes that has a long-time, logarithmic tail; this result is consistent with an earlier study on burglaries. This long-tail feature cannot be reproduced by the other standard methods. In addition, a merit of the Green's function method is the low computational cost in the case of high density of events and/or large amount of the training data.

stat.AP↗

arXiv2016

A fundamental mechanism for carbon-film lubricity identified by means of ab initio molecular dynamics

Different hypotheses have been proposed to explain the mechanism for the extremely low friction coefficient of carbon coatings and its undesired dependence on air humidity. A decisive atomistic insight is still lacking because of the difficulties in monitoring what actually happens at the buried sliding interface. Here we perform large-scale ab initio molecular dynamics simulations of both undoped and silicon-doped carbon films sliding in the presence of water. We observe the tribologically-induced surface hydroxylation and subsequent formation of a thin film of water molecules bound to the OH-terminated surface by hydrogen bonds. The comparative analysis of silicon-incorporating and clean surfaces, suggests that this two-step process can be the key phenomenon to provide high slipperiness to the carbon coatings. The water layer is, in fact, expected to shelter the carbon surface from direct solid-on-solid contact and make any counter surface slide extremely easily on it. The present insight into the wettability of carbon-based films can be useful for designing new coatings for biomedical and energy-saving applications with environmental adaptability.

cond-mat.mes-hall↗

arXiv2015

Insigths into the tribochemistry of silicon-doped carbon based films by ab initio analysis of water/surface interactions

Diamond and diamond-like carbon (DLC) are used as coating materials for numerous applications, ranging from biomedicine to tribology. Recently, it has been shown that the hydrophilicity of the carbon films can be enhanced by silicon doping, which highly improves their biocompatibility and frictional performances. Despite the relevance of these properties for applications, a microscopic understanding on the effects of silicon is still lacking. Here we apply ab initio calculations to study the interaction of water molecules with Si-incorporated C(001) surfaces. We find that the presence of Si dopants considerably increases the energy gain for water chemisorption and decreases the energy barrier for water dissociation by more than 50%. We provide a physical rational for the phenomenon by analysing the electronic charge displacements occuring upon adsorption. We also show that once hydroxylated, the surface is able to bind further water molecules much strongly than the clean surface via hydrogen-bond networks. This two-step process is consistent with and can explain the enhanced hydrophilic character observed in carbon-based films doped by silicon.

cond-mat.mes-hall↗

arXiv2006

Ab-initio calculation method for charged slab systems using field-induced gaussian sheet

A new repeated-slab calculation method is developed to simulate the electronic structures of charged surfaces by arranging density-variable charged sheets in vacuum regions to realize a constant potential on the charged sheets and maintain the charge neutrality condition. The charged sheets are fabricated so as to screen an electric field from charged slabs; consequently, they act like a counter electrode composed of flat perfect conductors, modeling a tip of a scanning tunneling microscope or a reference electrode in an electrochemical cell. This method has the advantages of ease of implementation into existing repeated-slab programs and low computational costs.

cond-mat.mtrl-sci↗