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arXiv 2024年

作者： Ruan, Xinhui Li, Li Liang, Guihan Zhao, Silu Wang, Jia-Heng Bu, Yizhou Chen, Bingjie Song, Xiaohui Li, Xiang Zhang, He Wang, Jinzhe Zhao, Qianchuan Xu, Kai Fan, Heng Liu, Yu-Xi Zhang, Jing Peng, Zhihui Xiang, Zhongcheng Zheng, Dongning Beijing National Laboratory for Condensed Matter Physics Institute of Physics Chinese Academy of Sciences Beijing100190 China Department of Automation Tsinghua University Beijing100084 China Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Key Laboratory for Matter Microstructure and Function of Hunan Province Department of Physics Synergetic Innovation Center for Quantum Effects and Applications Hunan Normal University Changsha410081 China School of Physical Sciences University of Chinese Academy of Sciences Beijing100049 China School of Integrated Circuits Tsinghua University Beijing100084 China Hefei National Laboratory Hefei230088 China School of Automation Science and Engineering Xi'an Jiaotong University Xi’an710049 China MOE Key Lab for Intelligent Networks and Network Security Xi'an Jiaotong University Xi’an710049 China CAS Center for Excellence in Topological Quantum Computation School of Physical Sciences University of Chinese Academy of Sciences Beijing100049 China

A hybrid system with tunable coupling between phonons and qubits shows great potential for advancing quantum information processing. In this work, we demonstrate strong and tunable coupling between a surface acoustic wave (SAW) resonator and a transmon qubit based on galvanic-contact flip-chip technique. The coupling strength varies from 2π×7.0 MHz to -2π×20.6 MHz, which is extracted from different vacuum Rabi oscillation frequencies. The phonon-induced ac Stark shift of the qubit at different coupling strengths is also shown. Our approach offers a good experimental platform for exploring quantum acoustics and hybrid systems. Copyright © 2024, The Authors. All rights reserved.

关键词： Hybrid systems

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Flow and elastic networks on the N-torus: Geometry, analysis, and computation

arXiv

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arXiv 2019年

作者： Jafarpour, Saber Huang, Elizabeth Y. Smith, Kevin D. Bullo, Francesco Center for Control Dynamical Systems and Computation Departments of Mechanical Engineering and of Electrical and Computer Engineering University of California Santa Barbara

Networks with phase-valued nodal variables are central in modeling several important societal and physical systems, including power grids, biological systems, and coupled oscillator networks. One of the distinctive features of phase-valued networks is the existence of multiple operating conditions corresponding to critical points of an energy function or feasible flows of a balance equation. For a network with phase-valued states, it is not yet fully understood how many operating conditions exist, how to characterize them, and how to compute them efficiently. A deeper understanding of feasible operating conditions, including their dependence upon network structures, may lead to more reliable and efficient network systems. This paper introduces flow and elastic network problems on the n-torus and provides a rigorous and comprehensive framework for their study. Based on a monotonicity assumption, this framework localizes the solutions, bounds their number, and leads to an algorithm to compute them. Our analysis is based on a novel winding partition of the n-torus into winding cells, induced by Kirchhoff's angle law for undirected graphs. The winding partition has several useful properties, including notably that, each winding cell contains at most one solution. The proposed algorithm is based on a novel contraction mapping and is guaranteed to compute all solutions. Finally, we apply our results to numerically study the active power flow equations in several test cases and estimate power capacity and congestion of a power network. Copyright © 2019, The Authors. All rights reserved.

关键词： Winding

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Prediction of fitness in bacteria with causal jump dynamic mode decomposition

Prediction of fitness in bacteria with causal jump dynamic m...

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American control Conference

作者： Shara Balakrishnan Aqib Hasnain Nibodh Boddupalli Dennis M. Joshy Robert G. Egbert Enoch Yeung Department of Electrical and Computer Engineering University of California Santa Barbara Department of Mechanical Engineering University of California Santa Barbara Environmental and Biological Sciences Directorate Pacific Northwest National Laboratory Richland WA Mechanical Engineering Center for Control Dynamical Systems and Computation and Biomolecular Science and Engineering University of California Santa Barbara

ISBN: (数字)9781538682661

ISBN: (纸本)9781538682678

In this paper, we consider the problem of learning a predictive model for population cell growth dynamics as a function of the media conditions. We first introduce a generic data-driven framework for training operator-theoretic models to predict cell growth rate. We then introduce the experimental design and data generated in this study, namely growth curves of Pseudomonas putida as a function of casein and glucose concentrations. We use a data driven approach for model identification, specifically the nonlinear autoregressive (NAR) model to represent the dynamics. We show theoretically that Hankel DMD can be used to obtain a solution of the NAR model. We show that it identifies a constrained NAR model and to obtain a more general solution, we define a causal state space system using 1-step, 2-step,..., τ-step predictors of the NAR model and identify a Koopman operator for this model using extended dynamic mode decomposition. The hybrid scheme we call causal-jump dynamic mode decomposition, which we illustrate on a growth profile or fitness prediction challenge as a function of different input growth conditions. We show that our model is able to recapitulate training growth curve data with 96.6% accuracy and predict test growth curve data with 91% accuracy.

关键词： health Growth curves SOLUTIONS(GENERAL) model identification bacteria data driven Pseudomonas putida

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System size identification from sinusoidal probing in diffusive complex networks

arXiv

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arXiv 2020年

作者： Tyloo, Melvyn Delabays, Robin School of Engineering University of Applied Sciences of Western Switzerland HES-SO SionCH-1951 Switzerland Automatic Control Laboratory ETH Zurich ZurichCH-8092 Switzerland Center for Control Dynamical Systems and Computation UC Santa Barbara Santa BarbaraCA93106-5070 United States

One of the most fundamental characteristic of a complex system is its size (or volume), which, in many modelling, is represented by the number of its individual components. Complex systems under investigation nowadays are typically large and/or time-varying, rendering their identification challenging. We propose here an accurate and efficient method to determine the size of (i.e., number of agents in) a complex, diffusively coupled dynamical system, that leverages the response of the system to an injected probing signal. For our method to be applicable, we rely on some assumptions on system's characteristics, namely, on the spectrum of the coupling graph and on the basin stability of its steady state(s). Even though such assumptions imply that our method cannot be applied to any instance of diffusively coupled group of dynamical agents, we argue that it covers relevant and interesting examples. Furthermore, the simplicity of the approach and its low computational complexity renders it very interesting for the systems to which it applies. © 2020, CC BY.

关键词： Complex networks

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Policy evaluation and seeking for multi-agent reinforcement learning via best response

arXiv

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arXiv 2020年

作者： Yan, Rui Duan, Xiaoming Shi, Zongying Zhong, Yisheng Marden, Jason R. Bullo, Francesco Department of Automation Tsinghua University Beijing100084 China Mechanical Engineering Department Center of Control Dynamical Systems and Computation UC Santa BarbaraCA93106-5070 United States Department of Electrical and Computer Engineering UC Santa Barbara CA93106-5070 United States

This paper introduces two metrics (cycle-based and memory-based metrics), grounded on a dynamical game-theoretic solution concept called sink equilibrium, for the evaluation, ranking, and computation of policies in multi-agent learning. We adopt strict best response dynamics (SBRD) to model selfish behaviors at a meta-level for multi-agent reinforcement learning. Our approach can deal with dynamical cyclical behaviors (unlike approaches based on Nash equilibria and Elo ratings), and is more compatible with single-agent reinforcement learning than α-rank which relies on weakly better responses. We first consider settings where the difference between largest and second largest underlying metric has a known lower bound. With this knowledge we propose a class of perturbed SBRD with the following property: only policies with maximum metric are observed with nonzero probability for a broad class of stochastic games with finite memory. We then consider settings where the lower bound for the difference is unknown. For this setting, we propose a class of perturbed SBRD such that the metrics of the policies observed with nonzero probability differ from the optimal by any given tolerance. The proposed perturbed SBRD addresses the opponent-induced non-stationarity by fixing the strategies of others for the learning agent, and uses empirical game-theoretic analysis to estimate payoffs for each strategy profile obtained due to the perturbation. Copyright © 2020, The Authors. All rights reserved.

关键词： Game theory

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Social power evolution in influence networks with stubborn individuals

arXiv

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arXiv 2019年

作者： Tian, Ye Jia, Peng Mirtabatabaei, Anahita Wang, Long Friedkin, Noah E. Bullo, Francesco Center for Complex Systems School of Mechanoelectronic Engineering Xidian University Xi’an710071 China Mechanical Engineering Department Center of Control Dynamical Systems and Computation UC Santa BarbaraCA93106 United States Mechanical Engineering Department Center of Control Dynamical Systems and Computation UC Santa BarbaraCA93106 United States Center for Systems and Control College of Engineering Peking University Beijing100871 China Department of Sociology Center for Control Dynamical Systems and Computation UC Santa BarbaraCA93106 United States

—This paper studies the evolution of social power in influence networks with stubborn individuals. Based on the Friedkin-Johnsen opinion dynamics and the reflected appraisal mechanism, two models are proposed over issue sequences and over a single issue, respectively. These models generalize the original DeGroot-Friedkin (DF) model by including stubbornness. To the best of our knowledge, this paper is the first attempt to investigate the social power evolution of stubborn individuals basing on the reflected appraisal mechanism. Properties of equilibria and convergence are provided. We show that the models have same equilibrium social power and convergence property, where the equilibrium social power depends only upon interpersonal influence and individuals’ stubbornness. Roughly speaking, more stubborn individual has more equilibrium social power. Moreover, unlike the DF model without stubbornness, we prove that for the models with stubbornness, autocracy can never be achieved, while democracy can be achieved under any network topology. Copyright © 2019, The Authors. All rights reserved.

关键词： Dynamical systems

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Game Theoretic Potential Field for Autonomous Water Surface Vehicle Navigation Using Weather Forecasts

Game Theoretic Potential Field for Autonomous Water Surface ...

引用

American control Conference

作者： Evan Krell Luis Rodolfo Garcia Carrillo Scott A. King Joao P. Hespanha Control of Robots and Autonomous Agents Laboratory (CORAL) Texas A&M University Corpus Christi CORAL and the Unmanned Systems Laboratory (TAMUCC-USL) School of Engineering & Computing Sciences Texas A&M University Corpus Christi Center for Control Dynamical Systems and Computation University of California Santa Barbara CA USA

ISBN: (数字)9781538682661

ISBN: (纸本)9781538682678

Imperfect weather forecasts complicate robot planning. A conservative motion planning algorithm is developed to address uncertainty in autonomous boat missions. Dynamic Programming generates an optimal action for every location and game theory strategically handles uncertainty. Experimental results using Massachusetts Bay forecasts show the robot is able minimize the cost of worst-case weather.

关键词： Robots Planning Force Games Heuristic algorithms Weather forecasting

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Network inference using sinusoidal probing

arXiv

引用

arXiv 2020年

作者： Delabays, Robin Tyloo, Melvyn Automatic Control Laboratory Swiss Federal Institute of Technology ZurichCH-8092 Switzerland Center for Control Dynamical Systems and Computation UCSB Santa BarbaraCA93106-5070 United States School of Engineering University of Applied Sciences of Western Switzerland HES-SO SionCH-1951 Switzerland LausanneCH-1015 Switzerland

The aim of this manuscript is to present a non-invasive method to recover the network structure of a dynamical system. We propose to use a controlled probing input and to measure the response of the network, in the spirit of what is done to determine oscillation modes in large electrical networks. For a large class of dynamical systems, we show that this approach is analytically tractable and we confirm our findings by numerical simulations of networks of Kuramoto oscillators. Our approach also allows us to determine the number of agents in the network by probing and measuring a single one of them. © 2020, CC BY.

关键词： Dynamical systems

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Fault-Tolerant Quantum computation with Static Linear Optics

引用

PRX Quantum 2021年第4期2卷 040353-040353页

作者： Ilan Tzitrin Takaya Matsuura Rafael N. Alexander Guillaume Dauphinais J. Eli Bourassa Krishna K. Sabapathy Nicolas C. Menicucci Ish Dhand Xanadu Toronto Ontario M5G 2C8 Canada Department of Physics University of Toronto Toronto Ontario M5S 1A7 Canada Department of Applied Physics Graduate School of Engineering The University of Tokyo 7–3–1 Hongo Bunkyo-ku Tokyo 113–8656 Japan Centre for Quantum Computation and Communication Technology School of Science RMIT University Melbourne VIC 3000 Australia Center for Quantum Information and Control University of New Mexico Albuquerque New Mexico 87131 USA

The scalability of photonic implementations of fault-tolerant quantum computing based on Gottesman-Kitaev-Preskill (GKP) qubits is injured by the requirements of inline squeezing and reconfigurability of the linear optical network. In this work we propose a topologically error-corrected architecture that does away with these elements at no cost—in fact, at an advantage—to state preparation overheads. Our computer consists of three modules: a two-dimensional (2D) array of probabilistic sources of GKP states; a depth-four circuit of static beam splitters, phase shifters, and short delay lines; and a 2D array of homodyne detectors. The symmetry of our proposed circuit allows us to combine the effects of finite squeezing and uniform photon loss within the noise model, resulting in more comprehensive threshold estimates. These jumps over both architectural and analytical hurdles considerably expedite the construction of a photonic quantum computer.

关键词： Measurement-based quantum computing Optical quantum information processing Quantum computation Quantum information architectures & platforms Quantum information processing with continuous variables Topological quantum computing

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Topological Phase Transition and Geometrical Frustration in Fourier Photonic Simulator

arXiv

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arXiv 2025年

作者： Sun, Yuxuan Fan, Weiru Xu, Xingqi Wang, Da-Wei Lin, Hai-Qing Zhejiang Key Laboratory of Micro-Nano Quantum Chips and Quantum Control School of Physics State Key Laboratory for Extreme Photonics and Instrumentation Zhejiang University Hangzhou310027 China College of Optical Science and Engineering Zhejiang University Zhejiang Province Hangzhou310027 China Hefei National Laboratory Hefei230088 China CAS Center for Excellence in Topological Quantum Computation Beijing100190 China Institute for Advanced Study in Physics School of Physics Zhejiang University Hangzhou310058 China

XY models with continuous spin orientation play a pivotal role in understanding topological phase transitions and emergent frustration phenomena, such as superconducting and superfluid phase transitions. However, the complex energy landscapes arising from frustrated lattice geometries and competing spin interactions make these models computationally intractable. To address this challenge, we design a programmable photonic spin simulator capable of emulating XY models with tunable lattice geometries and spin couplings, allowing systematic exploration of their statistical behavior. We experimentally observe the Berezinskii-Kosterlitz-Thouless (BKT) transition in a square-lattice XY model with nearest-neighbor interactions, accurately determining its critical temperature. Expanding to frustrated systems, we implement the approach in triangular and honeycomb lattices, uncovering sophisticated phase transitions and frustration effects, which are consistent with theoretical predictions. This versatile platform opens avenues for probing unexplored XY model phenomena across diverse geometries and interaction schemes, with potential applications in solving complex optimization and machine learning problems. Copyright © 2025, The Authors. All rights reserved.

关键词： Topology

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