arXiv subjects

Hiroki Saito

Publications and source records attributed to Hiroki Saito.

At least 37 records · Page 2Linked to original sources

arXiv2022

Phase separation and multistability of two-component Bose-Einstein condensate in an optical cavity

We examine the multistability associated with miscibility-immiscibility conditions for a two-component Bose-Einstein condensate coupled to the light field in an optical cavity. For a strongly immiscible condition, the system exhibits a variety of density structures, including separated state, stripe state, and their coexistence. The multistability arises from these spatial structures of the two-component condensate, which significantly alter the hysteresis curve with respect to the intensity of cavity pumping. We present a variational approach to confirm our numerical results.

cond-mat.quant-gas↗

arXiv2021

Hydrodynamic generation of skyrmions in a two-component Bose-Einstein condensate

When an obstacle is moved in a superfluid faster than a critical velocity, quantized vortices are generated behind the obstacle. Here we propose a method to create more complicated topological excitations, three-dimensional skyrmions, behind a moving obstacle. We numerically show that, in a two-component Bose-Einstein condensate, component-dependent obstacle potentials can generate skyrmions in the wake, made up of quantized vortex rings in different components that are linked with each other. The lifetime of generated skyrmions can be prolonged by a guiding potential, which enables the formation of a skyrmion train.

cond-mat.quant-gas↗

arXiv2021

Interface properties in three-component Bose-Einstein condensates

Interface properties of a three-component Bose-Einstein condensate, in which component 3 is sandwiched by components 1 and 2 at the interface, are investigated. It is shown that component 3 can serve as a surfactant: the net interfacial tension is reduced by the presence of component 3. We calculate the interfacial tension as a function of the interaction coefficients. The stability of the interface is studied by Bogoliubov analysis. When the interfacial tension has a spatial gradient, interfacial flow is induced, which resembles the Marangoni flow.

cond-mat.quant-gas↗

arXiv2020

Adhesion mechanism of temperature effects on Sn coating on the carbon fiber reinforced polymer substrate by cold spray

Metalization of carbon fiber reinforced polymers (CFRPs) composites by the surface modification method to enhance their electrical conductivity, thermal conductivity, electromagnetic shielding, erosion, and radiation protection, has a significant meaning in the aerospace field. In this study, Sn coating was successfully fabricated on the CFRP composite substrate via low-pressure cold spray under four gas temperatures (473K, 523K, 573K, and 623K). Their bonding mechanism was explored via the surface observation after peel-off adhesion strength, accompanying with surface temperature distribution investigation. The results indicates that we cannot obtain coating at 623 K, the epoxy matrix of the CFRP substrate was gradually eroded during deposition over 523 K. Meanwhile, Sn particles melt under 623 K condition. Three kinds of interfaces: Sn/epoxy, Sn/CF, and Sn/CF/epoxy are revealed as characteristics with respect to different gas temperatures to explore the bonding mechanisms.

cond-mat.mtrl-sci↗

arXiv2020

Creation and manipulation of quantized vortices in Bose-Einstein condensates using reinforcement learning

We apply the technique of reinforcement learning to the control of nonlinear matter waves. In this method, an agent controls the position, strength, and shape of an external Gaussian potential to create and manipulate quantized vortices in a Bose-Einstein condensate (BEC) trapped in a harmonic potential. The density and velocity distributions of the BEC at each moment obtained by the Gross-Pitaevskii evolution are directly input into a convolutional neural network to determine the next action of the agent. We demonstrate that a stationary single-vortex state can be produced in a two-dimensional system, and a stationary vortex-ring state can be produced in a three-dimensional system.

cond-mat.quant-gas↗

arXiv2019

Neural-network quantum states at finite temperature

We propose a method to obtain the thermal-equilibrium density matrix of a many-body quantum system using artificial neural networks. The variational function of the many-body density matrix is represented by a convolutional neural network with two input channels. We first prepare an infinite-temperature state, and the temperature is lowered by imaginary-time evolution. We apply this method to the one-dimensional Bose-Hubbard model and compare the results with those obtained by exact diagonalization.

cond-mat.dis-nn↗

arXiv2019

Faraday patterns generated by Rabi oscillation in a binary Bose-Einstein condensate

The interaction between atoms in a two-component Bose-Einstein condensate (BEC) is effectively modulated by the Rabi oscillation. This periodic modulation of the effective interaction is shown to generate Faraday patterns through parametric resonance. We show that there are multiple resonances arising from the density and spin waves in a two-component BEC, and investigate the interplay between the Faraday-pattern formation and the phase separation.

cond-mat.quant-gas↗

arXiv2019

Symmetry-breaking instability of leapfrogging vortex rings in a Bose-Einstein condensate

Three coaxial quantized vortex rings in a Bose-Einstein condensate exhibit aperiodic leapfrogging dynamics. It is found that such circular vortex rings are dynamically unstable against deformation breaking axial rotational symmetry. The dynamics of the system are analyzed using the Gross-Pitaevskii and vortex-filament models. The dependence of the instability on the initial arrangement of the vortex rings is investigated. The system is found to be significantly unstable for a specific configuration of the three vortex rings.

cond-mat.quant-gas↗

arXiv2018

Interaction modulation in a long-lived Bose-Einstein condensate by rf coupling

We demonstrate modulation of the effective interaction between the magnetic sublevels of the hyperfine spin $F=1$ in a $^{87}$Rb Bose-Einstein condensate by Rabi coupling with radio-frequency (rf) field. The use of the $F=1$ manifold enables us to observe the long-term evolution of the system owing to the absence of inelastic collisional losses. We observe that the evolution of the density distribution reflects the change in the effective interaction between atoms due to rf coupling. We also realize a miscibility-to-immiscibility transition in the magnetic sublevels of $m = \pm 1$ by quenching the rf field. Rf-induced interaction modulation in long-lived states as demonstrated here will facilitate the study of out-of-equilibrium quantum systems.

physics.atom-ph↗

arXiv2018

Dissipation-assisted coherence formation in a spinor quantum gas

Dissipation affects all real-world physical systems and often induces energy or particle loss, limiting the efficiency of processes. Dissipation can also lead to the formation of dissipative structures or induce quantum decoherence. Quantum decoherence and dissipation are critical for quantum information processing. On the one hand, such effects can make achieving quantum computation much harder, but on the other hand, dissipation can promote quantum coherence and offer control over the system. It is the latter avenue -- how dissipation can be exploited to promote coherence in a quantum system -- that is explored in this work. We report the exploration of dissipation in a Bose-Einstein condensate (BEC) of spin-2 87Rb atoms. Through experiments and numerical simulations, we show that spin-dependent particle dissipation can give rise to quantum coherence and lead to the spontaneous formation of a magnetic eigenstate. Although the interactions between the atomic spins are not ferromagnetic, the spin-dependent dissipation enhances the synchronization of the relative phases among five magnetic sublevels, and this effects promotes magnetization.

cond-mat.quant-gas↗

arXiv2018

Orthogonal and antiparallel vortex tubes and energy cascades in quantum turbulence

We investigate the dynamics of energy cascades in quantum turbulence by directly observing the vorticity distributions in numerical simulations of the Gross-Pitaevskii equation. By Fourier filtering each scale of the vorticity distribution, we find that antiparallel vortex tubes at a large scale generate small-scale vortex tubes orthogonal to those at the large scale, which is a manifestation of the energy cascade from large to small scales. We reveal the dynamics of quantized vortex lines in these processes.

cond-mat.quant-gas↗

arXiv2018

Method to solve quantum few-body problems with artificial neural networks

A machine learning technique to obtain the ground states of quantum few-body systems using artificial neural networks is developed. Bosons in continuous space are considered and a neural network is optimized in such a way that when particle positions are input into the network, the ground-state wave function is output from the network. The method is applied to the Calogero-Sutherland model in one-dimensional space and Efimov bound states in three-dimensional space.

cond-mat.dis-nn↗

arXiv2018

Fingering instabilities and pattern formation in a two-component dipolar Bose-Einstein condensate

We study fingering instabilities and pattern formation at the interface of an oppositely polarized two-component Bose-Einstein condensate with strong dipole-dipole interactions in three dimensions. It is shown that the rotational symmetry is spontaneously broken by fingering instability when the dipole-dipole interactions are strengthened. Frog-shaped and mushroom-shaped patterns emerge during the dynamics due to the dipolar interactions. We also demonstrate the spontaneous density modulation and domain growth of a two-component dipolar BEC in the dynamics. Bogoliubov analyses in the two-dimensional approximation are performed, and the characteristic lengths of the domains are estimated analytically. Patterns resembling those in magnetic classical fluids are modulated when the number ratio of atoms, the trap ratio of the external potential, or tilted polarization with respect to the z direction is varied.

cond-mat.quant-gas↗

arXiv2017

Ground-state phases of a mixture of spin-1 and spin-2 Bose-Einstein condensates

We investigate the ground-state phases of a mixture of spin-1 and spin-2 Bose-Einstein condensates at zero magnetic field. In addition to the intra-spin interactions, two spin-dependent interaction coefficients are introduced to describe the inter-spin interaction. We systematically explore the wide parameter space, and obtain phase diagrams containing a rich variety of phases. For example, there exists a phase in which the spin-1 and spin-2 vectors are tilted relative to each other breaking the axial symmetry.

cond-mat.quant-gas↗

arXiv2017

Fractional maximal operators with weighted Hausdorff content

Let $n\ge 2$ be the spatial dimension. The purpose of this note is to obtain some weighted estimates for the fractional maximal operator ${\mathfrak M}α$ of order $α$, $0\leα<n$, on the weighted Choquet-Lorentz space $L^{p,q}(H_{w}^{d})$, where the weight $w$ is arbitrary and the underlying measure is the weighted $d$-dimensional Hausdorff content $H^{d}_{w}$, $0<d\le n$. Concerning a dependence of two parameters $α$ and $d$, we establish a general form of the Fefferman-Stein type inequalities for ${\mathfrak M}_α$. Our results contain the works of Adams, \cite{Ad} and of Orobitg and Verdera \cite{OV} as the special cases. Our results also imply the Tang result \cite{Ta}, if we assume the weight $w$ is in the Muckenhoupt $A_{1}$-class.

math.FA↗

arXiv2017

Machine learning technique to find quantum many-body ground states of bosons on a lattice

We develop a variational method to obtain many-body ground states of the Bose-Hubbard model using feedforward artificial neural networks. A fully-connected network with a single hidden layer works better than a fully-connected network with multiple hidden layers, and a multi-layer convolutional network is more efficient than a fully-connected network. AdaGrad and Adam are optimization methods that work well. Moreover, we show that many-body ground states with different numbers of atoms can be generated by a single network.

cond-mat.dis-nn↗

arXiv2017

Accurate estimation of interface phase diagram from STM images

We propose a new approach to constructing a phase diagram using the effective Hamiltonian derived only from a single real-space image produced by scanning tunneling microscopy (STM). Currently, there have been two main methods to construct phase diagrams in material science: ab initio calculations and CALPHAD with thermodynamic information obtained by experiments and/or theoretical calculations. Although the two methods have successfully revealed a number of unsettled phase diagrams, their results sometimes contradicted when it is difficult to construct an appropriate Hamiltonian that captures the characteristics of materials, e.g., for a system consisting of multiple-scale objects whose interactions are essential to the system's characteristics. Meanwhile, the advantage of our approach over existing methods is that it can directly and uniquely determine the effective Hamiltonian without any thermodynamic information. The validity of our approach is demonstrated through an Mg--Zn--Y long-period stacking-ordered structure, which is a challenging system for existing methods, leading to contradictory results. Our result successfully reproduces the ordering tendency seen in STM images that previous theoretical study failed to reproduce and clarifies its previously unknown phase diagram. Thus, our approach can be used to clear up contradictions shown by existing methods.

cond-mat.mtrl-sci↗

arXiv2017

Solving the Bose-Hubbard model with machine learning

Motivated by the recent successful application of artificial neural networks to quantum many-body problems [G. Carleo and M. Troyer, Science {\bf 355}, 602 (2017)], a method to calculate the ground state of the Bose-Hubbard model using a feedforward neural network is proposed. The results are in good agreement with those obtained by exact diagonalization and the Gutzwiller approximation. The method of neural-network quantum states is promising for solving quantum many-body problems of ultracold atoms in optical lattices.

cond-mat.dis-nn↗