A5053056661- Quantum many-body systems
- Quantum Computing Algorithms and Architecture
- Quantum and electron transport phenomena
- Model Reduction and Neural Networks
- Computational Physics and Python Applications
- Tensor decomposition and applications
- Physics of Superconductivity and Magnetism
- Machine Learning in Healthcare
- Stochastic processes and financial applications
- Quantum, superfluid, helium dynamics
- Quantum Information and Cryptography
Saitama University
2022-2025
Space-time dependence of imaginary-time propagators, vital for ab initio and many-body calculations based on quantum field theories, has been revealed to be compressible using Quantum Tensor Trains (QTTs) [Phys. Rev. X 13, 021015 (2023)]. However, the impact system parameters, like temperature, data size remains underexplored. This paper provides a comprehensive numerical analysis compactness local propagators in QTT one-time/-frequency objects two-time/-frequency objects, considering...
The correlation functions of quantum systems—central objects in field theories—are defined high-dimensional space-time domains. Their numerical treatment thus suffers from the curse dimensionality, which hinders application sophisticated many-body theories to interesting problems. Here, we propose a multiscale ansatz for systems based on quantics tensor trains (QTTs), "qubits" describing exponentially different length scales. then assumes separation scales by decomposing resulting tensors...
Quantitative descriptions of strongly correlated materials pose a considerable challenge in condensed matter physics and chemistry. A promising approach to address this problem is quantum embedding methods. In particular, the dynamical mean-field theory (DMFT) maps original system an effective impurity model comprising orbitals embedded electron bath. The biggest bottleneck DMFT calculations numerically solving model, i.e., computing Green's function. Past studies have proposed theoretical...
Tensor cross interpolation (TCI) is a powerful technique for learning tensor train (TT) by adaptively sampling target based on an formula. However, when the evaluations contain random noise, optimizing TT more advantageous than interpolating noise. Here, we propose new method that starts with initial guess of and optimizes it using non-linear least-squares fitting to measured points obtained from TCI. We use quantics TCI (QTCI) in this demonstrate its effectiveness sine two-time correlation...
A long-standing issue in mathematical finance is the speed-up of pricing options, especially multi-asset options. recent study has proposed to use tensor train learning algorithms speed up Fourier transform (FT)-based option pricing, utilizing ability networks compress high-dimensional tensors. Another usage network functions, including their parameter dependence. In this study, we propose a method, where, by algorithm, build trains that approximate functions appearing FT-based with...
Predicting the properties of strongly correlated materials is a significant challenge in condensed matter theory. The widely used dynamical mean-field theory faces difficulty solving quantum impurity models numerically. Hybrid quantum-classical algorithms such as variational eigensolvers emerge potential solution for models. A common these rapid growth number parameters with spin-orbitals impurity. In our approach to this problem, we develop compact using combination two different...
Space-time dependence of imaginary-time propagators, vital for \textit{ab initio} and many-body calculations based on quantum field theories, has been revealed to be compressible using Quantum Tensor Trains (QTTs) [Phys. Rev. X {\bf 13}, 021015 (2023)]. However, the impact system parameters, like temperature, data size remains underexplored. This paper provides a comprehensive numerical analysis compactness local propagators in QTT one-time/-frequency objects two-time/-frequency objects,...
Correlation functions of quantum systems -- central objects in field theories are defined high-dimensional space-time domains. Their numerical treatment thus suffers from the curse dimensionality, which hinders application sophisticated many-body to interesting problems. Here, we propose a multi-scale ansatz for correlation based on quantics tensor trains (QTT), ``qubits'' describing exponentially different length scales. The then assumes separation scales by decomposing resulting tensors...
Predicting the properties of strongly correlated materials is a significant challenge in condensed matter theory. The widely used dynamical mean-field theory faces difficulty solving quantum impurity models numerically. Hybrid quantum--classical algorithms such as variational eigensolver emerge potential solution for models. A common these rapid growth number parameters with spin-orbitals impurity. In our approach to this problem, we develop compact ansatzes using combination two different...