2,889 research outputs found
Continuous Occupancy Mapping in Dynamic Environments Using Particles
Particle-based dynamic occupancy maps were proposed in recent years to model
the obstacles in dynamic environments. Current particle-based maps describe the
occupancy status in discrete grid form and suffer from the grid size problem,
wherein a large grid size is unfavorable for motion planning, while a small
grid size lowers efficiency and causes gaps and inconsistencies. To tackle this
problem, this paper generalizes the particle-based map into continuous space
and builds an efficient 3D egocentric local map. A dual-structure subspace
division paradigm, composed of a voxel subspace division and a novel
pyramid-like subspace division, is proposed to propagate particles and update
the map efficiently with the consideration of occlusions. The occupancy status
of an arbitrary point in the map space can then be estimated with the
particles' weights. To further enhance the performance of simultaneously
modeling static and dynamic obstacles and minimize noise, an initial velocity
estimation approach and a mixture model are utilized. Experimental results show
that our map can effectively and efficiently model both dynamic obstacles and
static obstacles. Compared to the state-of-the-art grid-form particle-based
map, our map enables continuous occupancy estimation and substantially improves
the performance in different resolutions.Comment: This paper has been accepted by IEEE Transactions on Robotic
Subspace Variational Quantum Simulator
Quantum simulation is one of the key applications of quantum computing, which
can accelerate research and development in chemistry, material science, etc.
Here, we propose an efficient method to simulate the time evolution driven by a
static Hamiltonian, named subspace variational quantum simulator (SVQS). SVQS
employs the subspace-search variational eigensolver (SSVQE) to find a
low-energy subspace and further extends it to simulate dynamics within the
low-energy subspace. More precisely, using a parameterized quantum circuit, the
low-energy subspace of interest is encoded into a computational subspace
spanned by a set of computational basis, where information processing can be
easily done. After the information processing, the computational subspace is
decoded to the original low-energy subspace. This allows us to simulate the
dynamics of low-energy subspace with lower overhead compared to existing
schemes. While the dimension is restricted for feasibility on near-term quantum
devices, the idea is similar to quantum phase estimation and its applications
such as quantum linear system solver and quantum metropolis sampling. Because
of this simplicity, we can successfully demonstrate the proposed method on the
actual quantum device using Regetti Quantum Cloud Service. Furthermore, we
propose a variational initial state preparation for SVQS, where the initial
states are searched from the simulatable eigensubspace. Finally, we demonstrate
SVQS on Rigetti Quantum Cloud Service
Estimating Time-Varying Effective Connectivity in High-Dimensional fMRI Data Using Regime-Switching Factor Models
Recent studies on analyzing dynamic brain connectivity rely on sliding-window
analysis or time-varying coefficient models which are unable to capture both
smooth and abrupt changes simultaneously. Emerging evidence suggests
state-related changes in brain connectivity where dependence structure
alternates between a finite number of latent states or regimes. Another
challenge is inference of full-brain networks with large number of nodes. We
employ a Markov-switching dynamic factor model in which the state-driven
time-varying connectivity regimes of high-dimensional fMRI data are
characterized by lower-dimensional common latent factors, following a
regime-switching process. It enables a reliable, data-adaptive estimation of
change-points of connectivity regimes and the massive dependencies associated
with each regime. We consider the switching VAR to quantity the dynamic
effective connectivity. We propose a three-step estimation procedure: (1)
extracting the factors using principal component analysis (PCA) and (2)
identifying dynamic connectivity states using the factor-based switching vector
autoregressive (VAR) models in a state-space formulation using Kalman filter
and expectation-maximization (EM) algorithm, and (3) constructing the
high-dimensional connectivity metrics for each state based on subspace
estimates. Simulation results show that our proposed estimator outperforms the
K-means clustering of time-windowed coefficients, providing more accurate
estimation of regime dynamics and connectivity metrics in high-dimensional
settings. Applications to analyzing resting-state fMRI data identify dynamic
changes in brain states during rest, and reveal distinct directed connectivity
patterns and modular organization in resting-state networks across different
states.Comment: 21 page
The Bose-Hubbard model is QMA-complete
The Bose-Hubbard model is a system of interacting bosons that live on the
vertices of a graph. The particles can move between adjacent vertices and
experience a repulsive on-site interaction. The Hamiltonian is determined by a
choice of graph that specifies the geometry in which the particles move and
interact. We prove that approximating the ground energy of the Bose-Hubbard
model on a graph at fixed particle number is QMA-complete. In our QMA-hardness
proof, we encode the history of an n-qubit computation in the subspace with at
most one particle per site (i.e., hard-core bosons). This feature, along with
the well-known mapping between hard-core bosons and spin systems, lets us prove
a related result for a class of 2-local Hamiltonians defined by graphs that
generalizes the XY model. By avoiding the use of perturbation theory in our
analysis, we circumvent the need to multiply terms in the Hamiltonian by large
coefficients
Covalency and the metal-insulator transition in titanate and vanadate perovskites
A combination of density functional and dynamical mean-field theory is
applied to the perovskites SrVO, LaTiO and LaVO. We show that
DFT+DMFT in conjunction with the standard fully localized-limit (FLL)
double-counting predicts that LaTiO and LaVO are metals even though
experimentally they are correlation-driven ("Mott") insulators. In addition,
the FLL double counting implies a splitting between oxygen and transition
metal levels which differs from experiment. Introducing into the theory an
\textit{ad hoc} double counting correction which reproduces the experimentally
measured insulating gap leads also to a - splitting consistent with
experiment if the on-site interaction is chosen in a relatively narrow
range ( eV). The results indicate that these early transition
metal oxides will serve as critical test for the formulation of a general
\textit{ab initio} theory of correlated electron metals.Comment: 5 pages, 3 figure
Importance of many body effects in the kernel of hemoglobin for ligand binding
We propose a mechanism for binding of diatomic ligands to heme based on a
dynamical orbital selection process. This scenario may be described as bonding
determined by local valence fluctuations. We support this model using
linear-scaling first-principles calculations, in combination with dynamical
mean-field theory, applied to heme, the kernel of the hemoglobin metalloprotein
central to human respiration. We find that variations in Hund's exchange
coupling induce a reduction of the iron 3d density, with a concomitant increase
of valence fluctuations. We discuss the comparison between our computed optical
absorption spectra and experimental data, our picture accounting for the
observation of optical transitions in the infrared regime, and how the Hund's
coupling reduces, by a factor of five, the strong imbalance in the binding
energies of heme with CO and O_2 ligands.Comment: 5 pages, 4 figures. Supplementary material 12 pages, 5 figures. This
version (v2) matches that accepted for Physical Review Letters on 31 January
201
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