169 research outputs found
Estimating Entanglement Entropy via Variational Quantum Circuits with Classical Neural Networks
Entropy plays a crucial role in both physics and information science,
encompassing classical and quantum domains. In this work, we present the
Quantum Neural Entropy Estimator (QNEE), a novel approach that combines
classical neural network (NN) with variational quantum circuits to estimate the
von Neumann and Renyi entropies of a quantum state. QNEE provides accurate
estimates of entropy while also yielding the eigenvalues and eigenstates of the
input density matrix. Leveraging the capabilities of classical NN, QNEE can
classify different phases of quantum systems that accompany the changes of
entanglement entropy. Our numerical simulation demonstrates the effectiveness
of QNEE by applying it to the 1D XXZ Heisenberg model. In particular, QNEE
exhibits high sensitivity in estimating entanglement entropy near the phase
transition point. We expect that QNEE will serve as a valuable tool for quantum
entropy estimation and phase classification.Comment: 14 pages, 5 figures; see also independent researches of Shin, Lee,
and Jeong at arXiv:2306.14566v1 and Goldfeld, Patel, Sreekumar, Wilde at
arXiv:2307.0117
HOPES AND OPPORTUNITIES FOR INNER CITY RESIDENTS: Temporal and Spatial Assessment of Racial and Socioeconomic Conditions of Neighborhoods Adjacent to Brownfields in the Detroit Metropolitan Area.
Although many environmental justice studies have examined racial and socioeconomic disparities in locations of hazardous waste facilities, no study has examined to date racial and socioeconomic disparities in brownfield locations. In order to fill this gap, this dissertation thus examines the racial and socioeconomic characteristics of neighborhoods adjacent to brownfields in the Detroit region from 1960 to 2000. Based on some of the past sociological claims in the specific context of brownfields, this dissertation argues that deindustrialization in the 1960s and the subsequent concentration of poverty in the 1970s were responsible for socioeconomic disparities in brownfield locations. That is, socioeconomic conditions of brownfield neighborhoods are worse than socioeconomic conditions of non-brownfield neighborhoods. Moreover, this dissertation also argues that residential segregation imposed on minorities also was responsible for racial disparities in brownfield locations, meaning that brownfield neighborhoods are minority concentrated compared to non-brownfield neighborhoods.
This dissertation combines the locations of brownfields provided by the Michigan Department of Environmental Quality with 1960, 1970, 1980, 1990, and 2000 US Census data employing distance-based methods. Results reveal that brownfield neighborhoods show a higher concentration of minorities and a lower socioeconomic condition than non-brownfield neighborhoods. In addition, race is the strongest independent predictor of brownfield locations. Longitudinal analyses of brownfield locations from 1960 to 2000 reveal that brownfield neighborhoods experienced greater socioeconomic decline than did non-brownfield neighborhoods. When socioeconomic characteristics in 1970 are controlled, distinctive patterns of subsequent changes in socioeconomic characteristics were found on the basis of initial socioeconomic status. For the wealthiest neighborhoods in 1970, brownfield neighborhoods experienced greater socioeconomic declines than non-brownfield neighborhoods only in the 1970s. For second and third wealthiest neighborhoods, brownfield neighborhoods experienced greater socioeconomic declines than non-brownfield neighborhoods in both the 1970s and 1980s. For the most impoverished neighborhoods, no significant changes in socioeconomic differences between brownfield and non-brownfield neighborhoods were found in any decade. Finally, impoverished and minority-concentrated neighborhoods tend to get priority in brownfield cleanup. Findings from this dissertation suggest that deindustrialization led not only to economic and social inequality but also to environmental inequality.Ph.D.Natural Resources and EnvironmentUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/60767/1/sangyunl_1.pd
Progressive Deblurring of Diffusion Models for Coarse-to-Fine Image Synthesis
Recently, diffusion models have shown remarkable results in image synthesis
by gradually removing noise and amplifying signals. Although the simple
generative process surprisingly works well, is this the best way to generate
image data? For instance, despite the fact that human perception is more
sensitive to the low frequencies of an image, diffusion models themselves do
not consider any relative importance of each frequency component. Therefore, to
incorporate the inductive bias for image data, we propose a novel generative
process that synthesizes images in a coarse-to-fine manner. First, we
generalize the standard diffusion models by enabling diffusion in a rotated
coordinate system with different velocities for each component of the vector.
We further propose a blur diffusion as a special case, where each frequency
component of an image is diffused at different speeds. Specifically, the
proposed blur diffusion consists of a forward process that blurs an image and
adds noise gradually, after which a corresponding reverse process deblurs an
image and removes noise progressively. Experiments show that the proposed model
outperforms the previous method in FID on LSUN bedroom and church datasets.
Code is available at https://github.com/sangyun884/blur-diffusion
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