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 La2CuO4{\text{La}}_{2}{\text{CuO}}_{4}

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 H$_3$S$^+$ 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

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