Although kagome lattice antiferromagnets are expected to host a wealth of quantum phases, many aspects of their physical properties remain unresolved, requiring further investigation. In this poster, we focus on the thermal Hall conductivity, which contains information about quasiparticle excitations, and report finite-temperature results obtained with tensor-network methods. In particular, we...
We address the problem of distinguishing two unknown quantum states with as few experiments as possible. Instead of estimating the full density matrix via tomography, we treat each measurement as an online decision-making problem. This problem can be formulated as a linear bandit, in which the probability of each outcome is a linear function of an unknown vector encoding the true state. Using...
This work presents an implementation of the Quantum Imaginary Time Evolution (QITE) and QLanczos algorithms on a 1D infinite-size lattice. We transform the uniform matrix product state (uMPS) into a quantum circuit and use the concept of the time-dependent variational principle (TDVP) to implement QITE. Our study includes simulation results for the transverse-field Ising model, obtained from...
Recently, a number of state-of-the-art hybrid parallel eigensolver libraries have been developed: EigenExa and ELPA for dense matrices, and Anasazi and SLEPc for sparse matrices, and so on. However, there are yet only a few application programs that use these solvers. This is partially due to the different function interfaces for each solver. Also, different compile and link procedures for...
In this study, we propose the k-Mixture Exponential Hopfield Network (kMEHN) as a framework that bridges the Classical Hopfield Network (CHN) and the Modern Hopfield Network (MHN) by integrating their structural characteristics.
The CHN defines an energy function using a single symmetric weight matrix, where stored patterns correspond to stable energy minima [1].
In contrast, the MHN...
We hypothesize that the expressive power of the quantum kernel space may be superior to that of the classical kernel space, and are studying quantum anomaly detection. Unlike factory products, quantum anomaly detection was applied to the image inspection process of various agricultural products with various standards. In this study, a learning model was constructed using various quantum kernel...
he development of 200 qubits or more quantum processors is divided into subtasks running in parallel to tackle the various technical and conceptual challenges. On the technical side, we are developing a compact 2D MOT, a science chamber for the 3D MOT with an ultrahigh vacuum environment, and a 2D tweezer array of neutral atoms. The 2D MOT segment improves the atom flux and segregates the...
We study how quantum measurements can transform quantum phases in spin ladder systems through an entanglement swapping protocol. Consecutive Bell measurements are performed between the legs of two independent ladders, followed by uniform post-selection of the measurement outcomes. We analyze the resulting phase realized in the unmeasured outer legs.
Our analysis is based on topological...
We present a novel approach that combines tensor networks with generating functions to compute the dynamical structure factor of one-dimensional quantum spin chains with spin values S=1/2,1,3/2, and 2. We will compare our results with those from the Lanczos method and linear spin wave theory. Furthermore, we will assess if the obtained spectral shapes and low-energy excitations align with...
Background
The Boltzmann machine is a generative machine learning model originated from the toy model of magnetic materials in statistical mechanics. It can approximate a probability distribution by adjusting the set of potential parameters and the number of units. In particular, over the last decade, there has been a significant amount of research on approximating ground state wave...
We employ convolutional neural network quantum states (NQS) to investigate the topological phase transition in the toric code when perturbed by isotropic Heisenberg interactions, a regime that has remained largely unexplored. Neural networks have recently demonstrated remarkable versatility in tackling quantum many-body problems, capturing volume-law entanglement and complex correlations [1]....
We present a Multilayer Variational Quantum Circuit (MLVQC) classifier designed to address the challenges of barren plateaus in quantum neural network training. Our approach builds upon the concept of Variational Quantum Circuits (VQC), which are promising candidates for hybrid quantum-classical computations, particularly under the constraints of noisy intermediate-scale quantum (NISQ)...
Tree tensor networks (TTNs) offer powerful models for image classification. While these TTN image classifiers already show excellent performance on classical hardware, embedding them into quantum neural networks (QNNs) may further improve the performance by leveraging quantum resources. However, embedding TTN classifiers into QNNs for multiclass classification remains challenging. Key...
We present a matrix-product-state-based approach for simulating a qubits-waveguide system in real space. In this representation, the photon mode is described as a Bogoliubov mode, and the vacuum of the waveguide also becomes the Bogoliubov vacuum. This entangled Bogoliubov vacuum makes a simulation difficult because it requires large bosonic degrees of freedom for a faithful representation of...
Tensor network approach provides us with a novel framework to study the field theories on a lattice without resorting to the probabilistic interpretation. Therefore, the tensor network approach is expected to be free from the sign problem. On the other hand, we have to introduce a reliable regularization scheme to discretize the continuous degrees of freedom, otherwise we cannot perform tensor...
Quantum magic, which accounts for the non-stabilizer content of a state, is essential for universal quantum computation beyond classically simulable resources. However, the way magic builds up during structured unitary dynamics remains largely open. Here, we investigate the generation and evolution of quantum magic in discrete-time quantum walks (DTQWs)--a simple, tunable unitary model...
