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Sample-efficient model-based reinforcement learning for quantum control

Physical Review Research 2023年第4期5卷 043002-043002页

作者： Irtaza Khalid Carrie A. Weidner Edmond A. Jonckheere Sophie G. Schirmer Frank C. Langbein School of Computer Science and Informatics Cardiff University Cardiff CF24 4AG United Kingdom Quantum Engineering Technology Laboratories H. H. Wills Physics Laboratory and Department of Electrical and Electronic Engineering University of Bristol Bristol BS8 1FD United Kingdom Department of Electrical and Computer Engineering University of Southern California Los Angeles California 90007 USA Department of Physics Swansea University Swansea SA2 8PP United Kingdom

We propose a model-based reinforcement learning (RL) approach for noisy time-dependent gate optimization with reduced sample complexity over model-free RL. Sample complexity is defined as the number of controller interactions with the physical system. Leveraging an inductive bias, inspired by recent advances in neural ordinary differential equations (ODEs), we use an autodifferentiable ODE, parametrized by a learnable Hamiltonian ansatz, to represent the model approximating the environment, whose time-dependent part, including the control, is fully known. Control alongside Hamiltonian learning of continuous time-independent parameters is addressed through interactions with the system. We demonstrate an order of magnitude advantage in sample complexity of our method over standard model-free RL in preparing some standard unitary gates with closed and open system dynamics, in realistic computational experiments incorporating single-shot measurements, arbitrary Hilbert space truncations, and uncertainty in Hamiltonian parameters. Also, the learned Hamiltonian can be leveraged by existing control methods like GRAPE (gradient ascent pulse engineering) for further gradient-based optimization with the controllers found by RL as initializations. Our algorithm, which we apply to nitrogen vacancy (NV) centers and transmons, is well suited for controlling partially characterized one- and two-qubit systems.

关键词： Machine learning Open quantum systems & decoherence Quantum computation Quantum control Quantum feedback Quantum information processing Quantum information theory Quantum parameter estimation Quantum tomography

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Embedding-aided network dismantling

arXiv

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arXiv 2022年

作者： Osat, Saeed Papadopoulos, Fragkiskos Teixeira, Andreia Sofia Radicchi, Filippo Göttingen37077 Germany Department of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology 33 Saripolou Street Limassol3036 Cyprus LASIGE Departamento de Informática Faculdade de Ciências Universidade de Lisboa Lisboa1749-016 Portugal Center for Complex Networks and Systems Research Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN47408 United States

Optimal percolation concerns the identification of the minimum-cost strategy for the destruction of any extensive connected components in a network. Solutions of such a dismantling problem are important for the design of optimal strategies of disease containment based either on immunization or social distancing. Depending on the specific variant of the problem considered, network dismantling is performed via the removal of nodes or edges, and different cost functions are associated to the removal of these microscopic elements. In this paper, we show that network representations in geometric space can be used to solve several variants of the network dismantling problem in a coherent fashion. Once a network is embedded, dismantling is implemented using intuitive geometric strategies. We demonstrate that the approach well suits both Euclidean and hyperbolic network embeddings. Our systematic analysis on synthetic and real networks demonstrates that the performance of embedding-aided techniques is comparable to, if not better than, the one of the best dismantling algorithms currently available on the market. Copyright © 2022, The Authors. All rights reserved.

关键词： Cost functions

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Performance optimization in mobile-edge computing via deep reinforcement learning

arXiv

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arXiv 2018年

作者： Chen, Xianfu Zhang, Honggang Wu, Celimuge Mao, Shiwen Ji, Yusheng Bennis, Mehdi VTT Technical Research Centre of Finland Finland College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Graduate School of Informatics and Engineering University of Electro-Communications Tokyo Japan Department of Electrical and Computer Engineering Auburn University AuburnAL United States Information Systems Architecture Research Division National Institute of Informatics Tokyo Japan Centre for Wireless Communications University of Oulu Finland

To improve the quality of computation experience for mobile devices, mobile-edge computing (MEC) is emerging as a promising paradigm by providing computing capabilities within radio access networks in close proximity. Nevertheless, the design of computation offloading policies for a MEC system remains challenging. Specifically, whether to execute an arriving computation task at local mobile device or to offload a task for cloud execution should adapt to the environmental dynamics in a smarter manner. In this paper, we consider MEC for a representative mobile user in an ultra dense network, where one of multiple base stations (BSs) can be selected for computation offloading. The problem of solving an optimal computation offloading policy is modelled as a Markov decision process, where our objective is to minimize the long-term cost and an offloading decision is made based on the channel qualities between the mobile user and the BSs, the energy queue state as well as the task queue state. To break the curse of high dimensionality in state space, we propose a deep Q-network-based strategic computation offloading algorithm to learn the optimal policy without having a priori knowledge of the dynamic statistics. Numerical experiments provided in this paper show that our proposed algorithm achieves a significant improvement in average cost compared with baseline policies. Copyright © 2018, The Authors. All rights reserved.

关键词： Reinforcement learning

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The Third International Conference on human-robot interaction

The Third International Conference on human-robot interactio...

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作者： Fong, Terry Dautenhahn, Kerstin Scheutz, Matthias Demiris, Yiannis Intelligent Robotics Group NASA Ames Research Center Department of Artificial Intelligence School of Computer Science University of Herfordshire Herfordshire United Kingdom Adaptive Systems Research Group University of Herfordshire Herfordshire United Kingdom Department of Cognitive Science Computer Science and Informatics Cognitive Science Program Indiana University Bloomington IN United States Intelligent Robotics Team Department of Electrical Engineering Imperial College London London United Kingdom

The third international conference on Human-Robot Interaction (HRI-2008) was held in Amsterdam, The Netherlands, March 12-15, 2008. The theme of HRI-2008, "living with robots," highlights the importance of the technical and social issues underlying human-robot interaction with companion and assistive robots for long-term use in everyday life and work activities. More than 250 researchers, practitioners, and exhibitors attended the conference, and many more contributed to the conference as authors or reviewers. HRI-2009 will be held in San Diego, California, from March 11-13, 2009. Copyright © 2008, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： Human robot interaction

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Temporal dynamics of connectivity and epidemic properties of growing networks

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Physical Review E 2016年第1期93卷 012301-012301页

作者： Babak Fotouhi Mehrdad Khani Shirkoohi Clinical and Health Informatics Group McGill University Montréal Québec Canada Department of Sociology McGill University Montréal Québec Canada Department of Electrical Engineering Sharif University of Technology Tehran Iran Department of Computer Science Sharif University of Technology Tehran Iran

Traditional mathematical models of epidemic disease had for decades conventionally considered static structure for contacts. Recently, an upsurge of theoretical inquiry has strived towards rendering the models more realistic by incorporating the temporal aspects of networks of contacts, societal and online, that are of interest in the study of epidemics (and other similar diffusion processes). However, temporal dynamics have predominantly focused on link fluctuations and nodal activities, and less attention has been paid to the growth of the underlying network. Many real networks grow: Online networks are evidently in constant growth, and societal networks can grow due to migration flux and reproduction. The effect of network growth on the epidemic properties of networks is hitherto unknown, mainly due to the predominant focus of the network growth literature on the so-called steady state. This paper takes a step towards alleviating this gap. We analytically study the degree dynamics of a given arbitrary network that is subject to growth. We use the theoretical findings to predict the epidemic properties of the network as a function of time. We observe that the introduction of new individuals into the network can enhance or diminish its resilience against endemic outbreaks and investigate how this regime shift depends upon the connectivity of newcomers and on how they establish connections to existing nodes. Throughout, theoretical findings are corroborated with Monte Carlo simulations over synthetic and real networks. The results shed light on the effects of network growth on the future epidemic properties of networks and offers insights for devising a priori immunization strategies.

关键词： Epidemic Network formation & growth

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Efficient Channel Assignment technique providing priority to Emergency Calls in Wireless Cellular Security Networks

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WSEAS Transactions on Communications 2006年第1期5卷 84-91页

作者： Ioannou, K. Dimitriadou, E. Ioannou, A. Panoutsopoulos, I. Garmpis, A. Kotsopoulos, S. Wireless Telecommunication Laboratory Department of Electrical and Computer Engineering University of Patras Rion 26500 Patras Greece OTE S.A. Messolonghi 30200 Greece Department of Applied Informatics in Management and Economy Technological Educational Institution of Messolonghi Messolonghi 30200 Greece

In this paper is presented a new efficient Channel Assignment technique. The philosophy of this technique is that provides priority to Emergency Calls in Wireless Cellular Security Networks. The new technique is based on a two-layer cellular architecture The lower layer of the proposed architecture is based on a microcellular solution, for absorbing the traffic loads of both the handoff and new calls of Emergency Calls (EC). The higher layer is based on a macro-cell umbrella solution, for absorbing the traffic load of the handoff and new calls of the Non Emergency Calls (NEC). The simulation results show that using the optimum number of channels in each layer, the QoS of EC and the Handoff Blocking Probability of NEC in a congested area where an extreme event was happened is optimized, having the minimum negative effect on the New Call Blocking Probability of the NEC.

关键词： Wireless telecommunication systems

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Knowledge sharing in flexible production networks: A context-based approach

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International Journal of Automotive Technology and Management 2009年第1期9卷 87-109页

作者： Smirnov, Alexander Levashova, Tatiana Shilov, Nikolay St. Petersburg Institute for Informatics and Automation RAS 39 14 line 199178 St. Petersburg Russia Russia St. Petersburg State Electrical Engineering University Russia Computer Aided Integrated Systems Laboratory SPIIRAS Russia

The appearance of production supply networks can be considered as a response to changes in global markets. Networks can be seen as a step beyond the linear supply chain topography. However, when dealing with multiple organisations and multiple processes within a complicated network, identifying and locating a member that has responsibility for and/or competence in a particular part of the network can be a laborious, time-consuming process. Knowledge sharing aims to assist in solving this problem. The approach presented here is based on the application of such technologies as ontology and context management to knowledge sharing. The paper also presents the experience of applying the above approach to solving a real-life problem from the area of dynamic logistics as part of a flexible production network configuration. The considered problem takes into account a continuously changing problem environment and requires nearly real-time solving. Copyright © 2009 Inderscience Enterprises Ltd.

关键词： Ontology

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Design and Implementation of Charging Circuit for Energy Storage System in Multi-Input Non-Isolated Converter with High Gain

Design and Implementation of Charging Circuit for Energy Sto...

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IEEE International Conference on Intelligent engineering Systems (INES)

作者： Donya Taheri Ghazanfar Shahgholian Mohammad Mehdi Mirtalaei Amirhosein Mosavi Arman Fathollahi Annamaria Varkonyi-Koczy Department of Electrical Engineering Najafabad Branch Islamic Azad University Najafabad Iran Smart Microgrid Research Center Najafabad Branch Islamic Azad University Najafabad Iran John von Neumann Faculty of Informatics Budapest Hungary University of Public Service Budapest Hungary Department of Electrical and Computer Engineering Aarhus University Aarhus Denmark John von Neumann Faculty of Informatics Obuda University Budapest Hungary

In this study, the topology of a multi-input high-gain non-isolated power converter is suggested. Within this converter, techniques involving voltage multiplier cells and a coupling inductor are utilized. It is possible to use switches that have low voltage stress and, as a result, low conductivity by combining these two methodologies. For applications dealing with clean energy, like green electrical power for public hospitals and etc., that consider a specific type of energy storage system, this particular configuration is an excellent choice. First, the charging performance of the energy storage system is explained, and then, for the design of the multi-input non-isolated power converter with a high voltage gain, the configuration and functionality of the proposed converter are fully investigated. This is accomplished so that the multi-input non-isolated converter can have high voltage gain. The correct operation of the converter is demonstrated through the presentation of a detailed design method, and numerical outcomes are shown for a variety of modes in which the converter can operate effectively. Eventually, to guarantee that the simulation outcomes are accurate, an experimental prototype of the proposed converter has been produced in a laboratory, and the results of the simulation have been validated with those obtained in practice.

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Weighted hypersoft configuration model

arXiv

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arXiv 2020年

作者： Voitalov, Ivan van der Hoorn, Pim Kitsak, Maksim Papadopoulos, Fragkiskos Krioukov, Dmitri Department of Physics Northeastern University BostonMA02115 United States Network Science Institute Northeastern University BostonMA02115 United States Department of Mathematics and Computer Science Eindhoven University of Technology Postbus 513 Eindhoven5600 MB Netherlands Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology 2628 CD Delft Netherlands Department of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology 33 Saripolou Street Limassol3036 Cyprus Department of Mathematics Northeastern University BostonMA02115 United States Department of Electrical and Computer Engineering Northeastern University BostonMA02115 United States

Maximum entropy null models of networks come in different flavors that depend on the type of the constraints under which entropy is maximized. If the constraints are on degree sequences or distributions, we are dealing with configuration models. If the degree sequence is constrained exactly, the corresponding microcanonical ensemble of random graphs with a given degree sequence is the configuration model per se. If the degree sequence is constrained only on average, the corresponding grandcanonical ensemble of random graphs with a given expected degree sequence is the soft configuration model. If the degree sequence is not fixed at all but randomly drawn from a fixed distribution, the corresponding hypercanonical ensemble of random graphs with a given degree distribution is the hypersoft configuration model, a more adequate description of dynamic real-world networks in which degree sequences are never fixed but degree distributions often stay stable. Here, we introduce the hypersoft configuration model of weighted networks. The main contribution is a particular version of the model with power-law degree and strength distributions, and superlinear scaling of strengths with degrees, mimicking the properties of some real-world networks. As a by-product, we generalize the notions of sparse graphons and their entropy to weighted networks. Copyright © 2020, The Authors. All rights reserved.

关键词： Machine learning

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AN INVESTIGATION ON EVOLUTIONARY IDENTIFICATION OF CONTINUOUS CHAOTIC SYSTEMS

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AIP Conference Proceedings 2011年第1期1337卷 280-284页

作者： Ivan Zelinka Donald Davendra Roman Senkerik Roman Jasek aDepartment of Informatics and Artificial Intelligence Tomas Bata University in Zlin Nad Stráněmi 4511 Zlin 76001 Czech Republic bDepartment of Computer Science Faculty of Electrical Engineering and Computer Science VSB‐TUO 17. listopadu 15 708 33 Ostrava‐Poruba Czech Republic

This paper discusses the possibility of using evolutionary algorithms for the reconstruction of chaotic systems. The main aim of this work is to show that evolutionary algorithms are capable of the reconstruction of chaotic systems without any partial knowledge of internal structure, i.e. based only on measured data. Algorithm SOMA was used in reported experiments here. Systems selected for numerical experiments here is the well‐known Lorenz system. For each algorithm and its version, repeated simulations were done, totaling 20 simulations. According to obtained results it can be stated that evolutionary reconstruction is an alternative and promising way as to how to identify chaotic systems.

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