Conveners
Contributed talks: Masahiro Hoshino , Rihito Sakurai , Kanta Masuki
- TBA (TBA)
Contributed talks
- There are no conveners in this block
Contributed talks
- There are no conveners in this block
Contributed talks
- There are no conveners in this block
Contributed talks
- There are no conveners in this block
Contributed talks
- There are no conveners in this block
Contributed talks
- There are no conveners in this block
Contributed talks
- There are no conveners in this block
Along with entanglement, nonstabilizerness (or magic) of quantum systems has been recognized as a crucial resource for achieving quantum computational advantage. The stabilizer Rényi entropy (SRE) has recently been established as a computationally tractable measure of nonstabilizerness, with numerical studies revealing universal behavior of the SRE in critical quantum spin chains. In this...
High dimensionality is one of the key challenge in mathematical finance, where option pricing and sensitivity computations must be both accurate and real‑time. Finite‑difference Monte Carlo methods often suffer numerical instability, especially for second‑order derivatives (a.k.a. Gamma). Chebyshev‑interpolation can stabilize sensitivities, but it suffers from the curse of dimensionality as...
The renormalization group (RG) framework, which establishes a connection between a microscopic model at short distances and its coarse-grained counterpart at larger scales, has been a pivotal tool for understanding many-body phenomena across vastly different scales, ranging from elementary particles to condensed matter. Central to the RG's success is its multiscale nature, enabling systems...
In this work we consider a particular class of Hamiltonians, known as stochastic matrix form (SMF) Hamiltonians, for which there is a systematic understanding of how to construct exact quantum many body scar (QMBS) states at zero energy. We study a particular example of a one-dimensional SMF Hamiltonian, for which there are QMBS subspaces that are connected through a Krammers-Wannier duality,...
Dynamical mean-field theory (DMFT) has been one of the most standard numerical methods for strongly correlated electron systems. We discuss that DMFT for interacting electrons with the semi-circle density of states can be viewed as a holographic renormalization group, similar to the holographic tree-tensor-network description of the Bethe lattice-type Ising model. In particular, the scaling...
Parton distribution functions (PDFs) describe universal properties of hadrons. They provide insights into the non-perturbative internal structure of bound states in high energy physics, and are highly significant for experiments. Calculating PDFs involves evaluating matrix elements with a Wilson line in a light-cone direction. This poses significant challenges for Monte Carlo methods in...
Reducing entanglement entropy is a key strategy for improving the efficiency of Matrix Product States (MPS), especially when simulating highly entangled quantum systems. Clifford-augmented MPS (CAMPS) is a recent approach that incorporates Clifford gates into the MPS ansatz to transform the basis in a way that compresses entanglement without altering the physical content of the state. This...
In strongly correlated electron systems, superconductivity and charge density waves often coexist in close proximity, suggesting a deeper relationship between these competing phases. Recent research indicates that these orders can intertwine, with the superconducting order parameter coupling to modulations in the electronic density. To elucidate this interplay, we study a two-dimensional XY...
In this presentation, we discuss the thermal Hall conductivity in the Kitaev model with additional interactions under a magnetic field, employing a finite-temperature tensor network method. We find that the thermal Hall conductivity divided by temperature, $\kappa_{xy}/T$, significantly overshoots the value of the half-integer quantization and exhibits a pronounced hump while decreasing...
We proposed the Adaptive Tensor Tree (ATT) method, which uses the tensor tree network within the Born machine framework to construct a generative model. This method expresses the target distribution function as the squared amplitude of a quantum wave function represented by a tensor tree. The core concept of the ATT method involves dynamically optimizing the tree structure to minimize the bond...
We propose an all-mode technique for Higher-Order Tensor Renormalization Group (HOTRG) by introducing a general framework for all-mode averaging in the coarse-graining step, utilizing a squeezer transformation. Since the all-mode approach yields numerical results that contain only statistical errors and are free from systematic errors, our results could be directly compared with exact...
In course registration, students face numerous challenges in course selection. In this study, we formulate these course-selection problems as a Quadratic Unconstrained Binary Optimization (QUBO) model, transforming course allocation into a complex combinatorial problem, and employ the Quantum Inspired Digital Annealing (QIDA) method for optimization.
While previous approaches such as...
The success of convolutional neural networks (CNN) in image classification has prompted the development of various quantum and quantum-inspired algorithms seeking to perform the very task and explore possible advantages. A recently proposed framework, quantum convolutional neural networks (QCNN), has found great potentials in solving physical problems, yet its capacity of performing other...
The projection and projective representation play a fundamental role in constructing and calculating models in physics. These techniques, combined with the analysis of entanglement and related variational techniques, resulted in various numerical techniques for realizing and analyzing renormalization group (RG) flows or quantum phase transitions.
In this presentation, we introduce a...
The quantum Gibbs state represents thermal equilibrium and is crucial in various fields. In this work, we analyze bipartite quantum correlations in quantum Gibbs states within long-range interacting systems and present an algorithm that constructs these states efficiently and accurately. First, we clarify the optimal condition under which the bipartite information measures adhere to the area...
The entanglement area law is a fundamental principle that defines the informational structure of quantum many-body systems and serves as the backbone for tensor network algorithms. Traditionally, this law has been established under two key assumptions: the system must have bounded local energy and exhibit short-range interactions. However, extending the area law to scenarios with unbounded...
We study the effect of a small density $n_v$ of quenched non-magnetic impurities, {\em i.e.} vacancy disorder, in gapped short-range resonating valence
bond (RVB) spin liquid states and valence bond solid (VBS) states of quantum magnets. We argue that a large class of short-range RVB liquids are stable at
small $n_v$ on the kagome lattice, while the corresponding states on triangular,...
In the time evolution following a quench of a low-entropy quantum state, the entanglement will generically grow with time. In a tensor network simulation of the time evolution, the bond dimension will thus need to grow if we want to accurately represent the time-evolved state. In this talk, we shall discuss optimized techniques for expanding the bond dimension in the time-dependent variational...
We propose a new class of typical quantum states, called Markov-shielded typical (MST) states, for one-dimensional quantum systems with open boundary conditions. Unlike conventional typicality arguments based on random sampling[1], MST states are derived from a variational principle that naturally connects to the variational formulation of ground states. This connection enables a numerical...
The Variational Quantum Algorithm (VQA) [1], which utilizes parameterized quantum circuits (PQCs), is one approach to solving problems that are challenging for classical computers using Noisy Intermediate-Scale Quantum (NISQ) devices. Utilizing gradients in VQA is expected to accelerate convergence [2]. However, as the number of parameters increases, the number of required quantum circuit...
The Variational Quantum Algorithm[1], VQA, is an algorithm to obtain a desirable quantum state by repeatedly updating parameters in its circuit. While it is analogous to the gradient-based machine learning, it has been a promissing algorithm that works on current Noisy Intermediate-Scale Quantum devices (NISQ)[2].
However, VQA training process has a significant problem, called barren...
