Conveners
Poster: Poster Session & Banquet
- There are no conveners in this block
Poster: Short Presentation
- Wei-Lin Tu (Keio University)
Neural Radiance Field (NeRF) is a well-known 3D reconstruction method capable of generating novel views of a target scene. NeRF model often employs a neural network trained by captured images to represent a 3D scene as a continuous function that maps a 3D coordinate and a view direction to color and density. In this work, we examine the potential of NeRF acceleration by replacing the MLP...
The search for Kitaev spin liquid states has recently broadened to include a number of honeycomb materials
with integer spin moments. The qualitative difference with their spin-1/2 counterparts is the presence of single-
ion anisotropy (SIA). This motivates our investigation of the effects of SIA on the ground state of the spin-1
Kitaev-Heisenberg (KH) model using the density-matrix...
Recently, Smith et al. investigated a topological phase transition in a spin chain by measuring string order parameters on the IBM quantum computers [Phys. Rev. Research 4, L022020 (2022)]. The measured quantities showed appreciable reduction from theoretical values, owing to inherent noise in the devices. In this work, we reproduce their results in the noisy quantum circuit simulator (Qiskit...
The XXZ spin chain model is a relevant benchmark for evaluating the effectiveness of adiabatic time evolution in quantum computers. In this study, we aim to estimate the evolution time required to reach the final ground state in adiabatic quantum dynamics of spin chains through classical matrix product state simulations. We configured the initial Hamiltonian as independent XX or Heisenberg...
We propose a loop optimization algorithm based on nuclear norm regularization for tensor network. The key ingredient of this scheme is to introduce a rank penalty term proposed in the context of data processing. Compared to standard variational periodic matrix product states method, this algorithm can circumvent the local minima related to short-ranged correlation in a simpler fashion. We...
Frustration is a ubiquitous phenomenon in many-body physics that influences the nature of the system in a profound way with exotic emergent behavior. Despite its long research history, the analytical or numerical investigations on frustrated spin models remain a formidable challenge due to their extensive ground-state degeneracy. In this paper, we propose a unified tensor network theory to...
Recently, tensor networks have expanded beyond their initial application in quantum state compression, finding versatile uses in other fields of physics including image compression [1], turbulence [2], and quantum field theory [3,4]. Among these applications, it has been revealed that tensor networks can efficiently compress functions with a low-rank structure into an operable format....
The fast solution of option pricing is a critical issue in quantitative finance. In the case of multiple assets, the computational cost of numerical simulations increases with the number of assets. Recent research has shown the potential for speeding up Fourier-based option pricing [1] using a tensor network learning algorithm, namely, tensor cross interpolation [2]. Another advantage of the...
Spin wave theory (SWT), in particular at lowest order, is often used to extract spin exchanges from scattering experiments such as inelastic neutron scattering and resonant inelastic X-ray scattering. However, this approach has limitations in accounting for large quantum fluctuations and possible fractional excitations that go beyond a magnon description. To address this, we employ a...
Tensor network (TN) methods are attracting much attention as powerful tools for computing strongly correlated many-body problems. The partition function of classical statistical systems can be represented by the TN form. However, the contraction of a large TN still requires an exponentially large computational effort. The concept of the real-space renormalization group resolves this problem....
The replica exchange Monte Carlo method, or parallel tempering, is a widely used extended ensemble method to overcome the difficulty of sampling from a complex multi-modal target distribution typical in frustrated spin systems and protein folding. Replicas having different model parameters, such as the temperature of a system, are stochastically swapped using the Metropolis algorithm....
The ground states of low-dimensional quantum many-body systems exhibit theoretically fascinating behaviors, such as quantum spin liquids and symmetry-protected topological orders. In their theoretical analysis, entanglement entropy(EE) and spectrum(ES) often play an essential role as a quantitative indicator of the quantum many-body entanglement. Meanwhile, verifying quantum many-body...
The construction of quantum circuits for classical data using tensor network methods is attracting attention as a scalable methodology when approximation is possible. However, in order to ensure accuracy equivalent to classical calculations, it is necessary to decompose a multi-qubit quantum gate, which has an amount of information equivalent to classical information, into gates that can be...
Understanding the fundamental theory of preventing thermalization is crucial for practical quantum device development. In this work, we expand upon prior research on quantum many-body scars in the U(1) quantum link and quantum dimer models. By employing a graphical representation of the basis, we extend the analytical expressions for specific scars to encompass a broader family of such scars....
Quantum circuits for loading probability distributions into quantum states are essential subroutines in quantum algorithms used in physics, finance engineering, and machine learning. The ability to implement these with high accuracy in shallow quantum circuits is a critical issue. We propose a novel quantum state preparation method for probability distribution with mirror symmetry using matrix...
A magnetic impurity in a metal is screened by conduction electrons via the Kondo effect. However, if the local density of states (LDOS) of the electrons near the impurity exhibits a pseudogap or hard gap at the Fermi level, the screening becomes imperfect, leading to a local moment (LM) phase. It has been believed that in the LM phase, the impurity spin is not screened but decoupled from the...
For realizing supersolid [1,2], many efforts have been made so far. Theoretically, short-range frustrated interactions in lattice Bose-Hubbard models play a key role to stabilize the supersolid states. Ultracold-atomic gases in optical lattices are promising experimental systems for the supersolid state. However, the presence of the dipole-dipole interactions is expected there. The effect of...
We study the ground-state phase diagram of a spin-$\frac12$ XXZ model with a chirality-chirality interaction (CCI) on a two-leg ladder. This model offers a minimal setup to study an interplay between spin and chirality degrees of freedom. The spin-chirality duality transformation allows us to relate the regimes of weak and strong CCIs. By applying the Abelian bosonization and the duality, we...
State-of-the-art experiments can controllably create Rydberg atoms inside a Bose-Einstein condensate (BEC) [1]. The large Rydberg electron orbital volume contains many neutral atoms, resulting in electron-atom scattering events. The number of atoms within the orbit, and hence the Rydberg-BEC interaction, can be tuned by choice of principal quantum number or condensate density. This makes the...
Finite size scaling analysis (FSS) provides a method to approach critical behavior using quantities in finite size systems. For computation, we employ the tensor network method and higher-order tensor renormalization group (HOTRG) to calculate various quantities for finite size systems. We demonstrate that finite size analysis enables us to accurately extract critical temperature, critical...
Tensor network and quantum computing are providing novel numerical approaches for lattice gauge theories in high-energy physics. These methods allow us to investigate the models suffering from the sign problem and many attempts have been made recently toward their applications to the QCD at finite density. For these future applications, it is necessary to establish how to deal with non-Abelian...
Matrix Product State (MPS) and Density Matrix Renormalization Group (DMRG) serve as effective variational techniques for investigating the low-energy states within quantum many-body systems, utilizing the underlying entanglement structures. By broadening the scope of MPS as a data representation framework, it becomes more adept at capturing intricate correlations within the system. Recent...
Combining the ideas of the imaginary-time evolution and the variational optimization of trial wave functions, we propose a generic construction of the variational tensor network operators[1] to study the quantum spin systems. We demonstrated that accurate variational ground state wave functions with extremely few tunable parameters can be obtained by applying these operators to some simple...
We conducted both unconstrained and symmetric infinite projected entangled pair state (iPEPS)[1] simulations for the honeycomb Kitaev model along out-of-plane external magnetic field h and the anisotropic interactions $Kz$ axes. In particular, for the $Kz/K > 2$ region where the Majorana fermion being gapped out and the low energy excitation dominated by the $Z_2$ fluxes, based on the gauge...
Volatility is of great importance for quantifying potential risk of financial assets. In empirical finance, usually volatility is estimated by a suitable model selected various existing volatility models. Here we model the volatility time series by quantum circuits. Using artificial volatility time series generated by the GARCH model often used in empirical finance, we perform the quantum...
