Coherent Transport of Quantum States by Deep Reinforcement Learning

Some problems in physics can be handled only after a suitable \textit{ansatz }solution has been guessed. Such method is therefore resilient to generalization, resulting of limited scope. The coherent transport by adiabatic passage of a quantum state through an array of semiconductor quantum dots provides a par excellence example of such approach, where it is necessary to introduce its so called counter-intuitive control gate ansatz pulse sequence. Instead, deep reinforcement learning technique has proven to be able to solve very complex sequential decision-making problems involving competition between short-term and long-term rewards, despite a lack of prior knowledge. We show that in the above problem deep reinforcement learning discovers control sequences outperforming the \textit{ansatz} counter-intuitive sequence. Even more interesting, it discovers novel strategies when realistic disturbances affect the ideal system, with better speed and fidelity when energy detuning between the ground states of quantum dots or dephasing are added to the master equation, also mitigating the effects of losses. This method enables online update of realistic systems as the policy convergence is boosted by exploiting the prior knowledge when available. Deep reinforcement learning proves effective to control dynamics of quantum states, and more generally it applies whenever an ansatz solution is unknown or insufficient to effectively treat the problem.Comment: 5 figure

Implementation and Integration of Fuzzy Algorithms for Descending Stair of KMEI Humanoid Robot

Locomotion of humanoid robot depends on the mechanical characteristic of the robot. Walking on descending stairs with integrated control systems for the humanoid robot is proposed. The analysis of trajectory for descending stairs is calculated by the constrains of step length stair using fuzzy algorithm. The established humanoid robot on dynamically balance on this matter of zero moment point has been pretended to be consisting of single support phase and double support phase. Walking transition from single support phase to double support phase is needed for a smooth transition cycle. To accomplish the problem, integrated motion and controller are divided into two conditions: motion working on offline planning and controller working online walking gait generation. To solve the defect during locomotion of the humanoid robot, it is directly controlled by the fuzzy logic controller. This paper verified the simulation and the experiment for descending stair of KMEI humanoid robot.&nbsp

QMCPACK: Advances in the development, efficiency, and application of auxiliary field and real-space variational and diffusion Quantum Monte Carlo

We review recent advances in the capabilities of the open source ab initio Quantum Monte Carlo (QMC) package QMCPACK and the workflow tool Nexus used for greater efficiency and reproducibility. The auxiliary field QMC (AFQMC) implementation has been greatly expanded to include k-point symmetries, tensor-hypercontraction, and accelerated graphical processing unit (GPU) support. These scaling and memory reductions greatly increase the number of orbitals that can practically be included in AFQMC calculations, increasing accuracy. Advances in real space methods include techniques for accurate computation of band gaps and for systematically improving the nodal surface of ground state wavefunctions. Results of these calculations can be used to validate application of more approximate electronic structure methods including GW and density functional based techniques. To provide an improved foundation for these calculations we utilize a new set of correlation-consistent effective core potentials (pseudopotentials) that are more accurate than previous sets; these can also be applied in quantum-chemical and other many-body applications, not only QMC. These advances increase the efficiency, accuracy, and range of properties that can be studied in both molecules and materials with QMC and QMCPACK

How to make modal shift from road to rail possible in the European transport market, as aspired to in the EU Transport White Paper 2011

The total demand for freight transport in Europe has increased significantly in recent decades, but most of it has been handled by road transport. To fulfil the modal shift targets set in the EU White Paper 2011, it will be necessary to double rail’s market share from today’s 18 %, by 2050. Translating this into reality means rail will have to handle 3 to 4 times the cargo volume it does today. With this in mind, the paper develops a vision of an efficient rail freight system in 2050. Methodology To achieve the above objective, the research applies literature survey and group discussion methodology and applying a system approach. Keeping on board the EU Transport White Paper 2011 modal shift targets, as well as future freight demand and customer requirements, the current research attempts to answer the following three critical questions: -How can rail offer the quality of service that will attract customers and fulfil the targets? - How can rail offer its customers a price that is competitive with road? - How can rail offer the capacity to meet the increased demand from modal shift

Tropical Kraus maps for optimal control of switched systems

Kraus maps (completely positive trace preserving maps) arise classically in quantum information, as they describe the evolution of noncommutative probability measures. We introduce tropical analogues of Kraus maps, obtained by replacing the addition of positive semidefinite matrices by a multivalued supremum with respect to the L\"owner order. We show that non-linear eigenvectors of tropical Kraus maps determine piecewise quadratic approximations of the value functions of switched optimal control problems. This leads to a new approximation method, which we illustrate by two applications: 1) approximating the joint spectral radius, 2) computing approximate solutions of Hamilton-Jacobi PDE arising from a class of switched linear quadratic problems studied previously by McEneaney. We report numerical experiments, indicating a major improvement in terms of scalability by comparison with earlier numerical schemes, owing to the "LMI-free" nature of our method.Comment: 15 page