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Found 18 papers, 1 papers with code
Spatio-temporal reconstruction of substance dynamics using compressed sensing in multi-spectral magnetic resonance spectroscopic imaging
The objective of our study is to observe dynamics of multiple substances in vivo with high temporal resolution from multi-spectral magnetic resonance spectroscopic imaging (MRSI) data.
Random Postprocessing for Combinatorial Bayesian Optimization
We find the postprocessing method significantly reduces the number of sequential steps to find the global optimum, especially when the acquisition function is of maximum a posterior estimation.
Assessment of image generation by quantum annealer
In this study, we focused on the performance of a quantum annealer as a generative model.
Kernel-based framework to estimate deformations of pneumothorax lung using relative position of anatomical landmarks
In video-assisted thoracoscopic surgeries, successful procedures of nodule resection are highly dependent on the precise estimation of lung deformation between the inflated lung in the computed tomography (CT) images during preoperative planning and the deflated lung in the treatment views during surgery.
Probing the Universality of Topological Defect Formation in a Quantum Annealer: Kibble-Zurek Mechanism and Beyond
We find that the degree of agreement with the experimental data from the D-Wave devices is better for the KZM, a quantum theory, than for the classical spin-vector Monte Carlo model, thus favoring a quantum description of the device.
Quantum Physics Statistical Mechanics
Message-passing algorithm of quantum annealing with nonstoquastic Hamiltonian
Hence, we developed a system with a time-dependent Hamiltonian consisting of a combination of the formulated Ising model and the "driver" Hamiltonian with only quantum fluctuation.
Optimization of neural networks via finite-value quantum fluctuations
We numerically test an optimization method for deep neural networks (DNNs) using quantum fluctuations inspired by quantum annealing.
Momentum-Space Renormalization Group Transformation in Bayesian Image Modeling by Gaussian Graphical Model
A new Bayesian modeling method is proposed by combining the maximization of the marginal likelihood with a momentum-space renormalization group transformation for Gaussian graphical models.
Deep Neural Network Detects Quantum Phase Transition
We detect the quantum phase transition of a quantum many-body system by mapping the observed results of the quantum state onto a neural network.
Deformation estimation of an elastic object by partial observation using a neural network
Deformation estimation of elastic object assuming an internal organ is important for the computer navigation of surgery.
Compressing Green's function using intermediate representation between imaginary-time and real-frequency domains
New model-independent compact representations of imaginary-time data are presented in terms of the intermediate representation (IR) of analytical continuation.
Sparse modeling approach to analytical continuation of imaginary-time quantum Monte Carlo data
A new approach of solving the ill-conditioned inverse problem for analytical continuation is proposed.
Quantum Monte Carlo simulation of a particular class of non-stoquastic Hamiltonians in quantum annealing
Quantum annealing is a generic solver of the optimization problem that uses fictitious quantum fluctuation.
Stochastic gradient method with accelerated stochastic dynamics
In this study, we propose violating the detailed balance condition to enhance the mixing rate.
L_1-regularized Boltzmann machine learning using majorizer minimization
In this study, we utilize the majorizer minimization method, which is a well-known technique implemented in optimization problems, to avoid the non-smoothness of the cost function.
Statistical-mechanical analysis of pre-training and fine tuning in deep learning
In this sense, we evaluate the efficacy of the unsupervised learning component of deep learning.
Inverse Renormalization Group Transformation in Bayesian Image Segmentations
A new Bayesian image segmentation algorithm is proposed by combining a loopy belief propagation with an inverse real space renormalization group transformation to reduce the computational time.
Detection of cheating by decimation algorithm
In this paper we aim to infer the correct biases and interactions of our model by considering a relatively small number of sets of training data.
