24 research outputs found
Dynamical screening in La2CuO4
We show that the dynamical screening of the Coulomb interaction among Cu-d
electrons in high-Tc cuprates is very strong and that a proper treatment of
this effect is essential for a consistent description of the electronic
structure. In particular, we find that ab-initio calculations for undoped
La2CuO4 yield an insulator only if the frequency dependence of the Coulomb
interaction is taken into account. We also identify a collective excitation in
the screened interaction at 9 eV which is rather localized on the copper site,
and which is responsible for a satellite structure at energy -13 eV, located
below the p bands
AdvantageNAS: Efficient Neural Architecture Search with Credit Assignment
Neural architecture search (NAS) is an approach for automatically designing a
neural network architecture without human effort or expert knowledge. However,
the high computational cost of NAS limits its use in commercial applications.
Two recent NAS paradigms, namely one-shot and sparse propagation, which reduce
the time and space complexities, respectively, provide clues for solving this
problem. In this paper, we propose a novel search strategy for one-shot and
sparse propagation NAS, namely AdvantageNAS, which further reduces the time
complexity of NAS by reducing the number of search iterations. AdvantageNAS is
a gradient-based approach that improves the search efficiency by introducing
credit assignment in gradient estimation for architecture updates. Experiments
on the NAS-Bench-201 and PTB dataset show that AdvantageNAS discovers an
architecture with higher performance under a limited time budget compared to
existing sparse propagation NAS. To further reveal the reliabilities of
AdvantageNAS, we investigate it theoretically and find that it monotonically
improves the expected loss and thus converges.Comment: preprint to be published in AAAI-2
Dynamical screening in
We show that the dynamical screening of the Coulomb interaction among Cu-d electrons in high-Tc cuprates is strong and that a proper treatment of this effect is essential for a consistent description of the electronic structure. In particular, we find that ab initio calculations for undoped La2CuO4 in the paramagnetic phase yield an insulator only if the frequency dependence of the Coulomb interaction and the interatomic interaction between p and d electrons are taken into account. We also identify a collective excitation in the screened interaction at 9 eV, which is rather localized on the copper site, and which is responsible for a satellite structure at energy −13 eV, located below the p bands
Resolution enhancement of one-dimensional molecular wavefunctions in plane-wave basis via quantum machine learning
Super-resolution is a machine-learning technique in image processing which
generates high-resolution images from low-resolution images. Inspired by this
approach, we perform a numerical experiment of quantum machine learning, which
takes low-resolution (low plane-wave energy cutoff) one-particle molecular
wavefunctions in plane-wave basis as input and generates high-resolution (high
plane-wave energy cutoff) wavefunctions in fictitious one-dimensional systems,
and study the performance of different learning models. We show that the
trained models can generate wavefunctions having higher fidelity values with
respect to the ground-truth wavefunctions than a simple linear interpolation,
and the results can be improved both qualitatively and quantitatively by
including data-dependent information in the ansatz. On the other hand, the
accuracy of the current approach deteriorates for wavefunctions calculated in
electronic configurations not included in the training dataset. We also discuss
the generalization of this approach to many-body electron wavefunctions.Comment: 13 pages, 18 figure
Max-Min Off-Policy Actor-Critic Method Focusing on Worst-Case Robustness to Model Misspecification
In the field of reinforcement learning, because of the high cost and risk of
policy training in the real world, policies are trained in a simulation
environment and transferred to the corresponding real-world environment.
However, the simulation environment does not perfectly mimic the real-world
environment, lead to model misspecification. Multiple studies report
significant deterioration of policy performance in a real-world environment. In
this study, we focus on scenarios involving a simulation environment with
uncertainty parameters and the set of their possible values, called the
uncertainty parameter set. The aim is to optimize the worst-case performance on
the uncertainty parameter set to guarantee the performance in the corresponding
real-world environment. To obtain a policy for the optimization, we propose an
off-policy actor-critic approach called the Max-Min Twin Delayed Deep
Deterministic Policy Gradient algorithm (M2TD3), which solves a max-min
optimization problem using a simultaneous gradient ascent descent approach.
Experiments in multi-joint dynamics with contact (MuJoCo) environments show
that the proposed method exhibited a worst-case performance superior to several
baseline approaches.Comment: Neural Information Processing Systems 2022 (NeurIPS '22
Quantum Computed Green's Functions using a Cumulant Expansion of the Lanczos Method
In this paper, we present a quantum computational method to calculate the
many-body Green's function matrix in a spin orbital basis. We apply our
approach to finite-sized fermionic Hubbard models and related impurity models
within Dynamical Mean Field Theory, and demonstrate the calculation of Green's
functions on Quantinuum's H1-1 trapped-ion quantum computer. Our approach
involves a cumulant expansion of the Lanczos method, using Hamiltonian moments
as measurable expectation values. This bypasses the need for a large overhead
in the number of measurements due to repeated applications of the variational
quantum eigensolver (VQE), and instead measures the expectation value of the
moments with one set of measurement circuits. From the measured moments, the
tridiagonalised Hamiltonian matrix can be computed, which in turn yields the
Green's function via continued fractions. While we use a variational algorithm
to prepare the ground state in this work, we note that the modularity of our
implementation allows for other (non-variational) approaches to be used for the
ground state.Comment: 20 pages, 12 figure
Quantum chemistry simulation of ground- and excited-state properties of the sulfonium cation on a superconducting quantum processor
The computational description of correlated electronic structure, and
particularly of excited states of many-electron systems, is an anticipated
application for quantum devices. An important ramification is to determine the
dominant molecular fragmentation pathways in photo-dissociation experiments of
light-sensitive compounds, like sulfonium-based photo-acid generators used in
photolithography. Here we simulate the static and dynamical electronic
structure of the HS molecule, taken as a minimal model of a
triply-bonded sulfur cation, on a superconducting quantum processor of the IBM
Falcon architecture.
To this end, we combine a qubit reduction technique with variational and
diagonalization quantum algorithms, and use a sequence of error-mitigation
techniques. We compute dipole structure factors and partial atomic charges
along ground- and excited-state potential energy curves, revealing the
occurrence of homo- and heterolytic fragmentation. To the best of our
knowledge, this is the first simulation of a photo-dissociation reaction on a
superconducting quantum device, and an important step towards the computational
description of photo-dissociation by quantum computing algorithms.Comment: 12 pages, 7 figure
Recommended from our members
Research and Design of a Routing Protocol in Large-Scale Wireless Sensor Networks
无线传感器网络,作为全球未来十大技术之一,集成了传感器技术、嵌入式计算技术、分布式信息处理和自组织网技术,可实时感知、采集、处理、传输网络分布区域内的各种信息数据,在军事国防、生物医疗、环境监测、抢险救灾、防恐反恐、危险区域远程控制等领域具有十分广阔的应用前景。 本文研究分析了无线传感器网络的已有路由协议,并针对大规模的无线传感器网络设计了一种树状路由协议,它根据节点地址信息来形成路由,从而简化了复杂繁冗的路由表查找和维护,节省了不必要的开销,提高了路由效率,实现了快速有效的数据传输。 为支持此路由协议本文提出了一种自适应动态地址分配算——ADAR(AdaptiveDynamicAddre...As one of the ten high technologies in the future, wireless sensor network, which is the integration of micro-sensors, embedded computing, modern network and Ad Hoc technologies, can apperceive, collect, process and transmit various information data within the region. It can be used in military defense, biomedical, environmental monitoring, disaster relief, counter-terrorism, remote control of haz...学位:工学硕士院系专业:信息科学与技术学院通信工程系_通信与信息系统学号:2332007115216
First-principles calculations of dynamical screened interactions for the transition metal oxides MO (M = Mn, Fe, Co, Ni)
To facilitate reliable and accurate modeling of the late transition metal oxides from first principles, we present detailed and systematic calculations of the dynamical screened Coulomb interactions (Hubbard U) of MnO, FeO, CoO, and NiO within the constrained random-phase approximation. The matrix elements of the screened interactions are calculated in maximally localized Wannier functions. We consider the screened interactions not only for conventional models that include only the d-like bands but also for models that include both the transition metal d bands and the oxygen p bands. The screened interaction is found to be sensitive to the screening channels subtracted from the polarization function. The frequency dependence of the screened interactions of these oxides is characterized by the two sharp peaks in low-energy region that may be called "subplasmons," which arise from the particle-hole transitions between d and oxygen p states. DOI: 10.1103/PhysRevB.87.16511