46 citations · 123 across the 20 of their papers we have counts for
22 papers
Mechanism-Aware Ensemble Conditioning for Data-Limited Emulation of Extreme Events
Isabella S. Thiel, Juan Bello-Rivas, Yannis G. Kevrekidis +1
Extreme events in chaotic systems are difficult to learn from short trajectories because they are controlled by transient finite-time instability rather than by frequently observed…
Excising dead components in the surface code using minimally invasive alterations: A performance study
Ryan V. Mishmash, Vadym Kliuchnikov, Juan Bello-Rivas +7
The physical implementation of a large-scale error-corrected quantum processor will necessarily need to mitigate the presence of defective (thereby "dead") physical components in i…
A Topologically Fault-Tolerant Quantum Computer with Four Dimensional Geometric Codes
David Aasen, Matthew B. Hastings, Vadym Kliuchnikov +8
Topological quantum codes are intrinsically fault-tolerant to local noise, and underlie the theory of topological phases of matter. We explore geometry to enhance the performance o…
Repeated ancilla reuse for logical computation on a neutral atom quantum computer
J. A. Muniz, D. Crow, H. Kim +59
Quantum processors based on neutral atoms trapped in arrays of optical tweezers have appealing properties, including relatively easy qubit number scaling and the ability to enginee…
Towards Coordinate- and Dimension-Agnostic Machine Learning for Partial Differential Equations
Trung V. Phan, George A. Kevrekidis, Soledad Villar +2
The machine learning methods for data-driven identification of partial differential equations (PDEs) are typically defined for a given number of spatial dimensions and a choice of…
Generative Learning for Slow Manifolds and Bifurcation Diagrams
Ellis R. Crabtree, Dimitris G. Giovanis, Nikolaos Evangelou +2
In dynamical systems characterized by separation of time scales, the approximation of so called ``slow manifolds'', on which the long term dynamics lie, is a useful step for model…
Roadmap to fault tolerant quantum computation using topological qubit arrays
David Aasen, Morteza Aghaee, Zulfi Alam +179
We describe a concrete device roadmap towards a fault-tolerant quantum computing architecture based on noise-resilient, topologically protected Majorana-based qubits. Our roadmap e…
Fault-tolerant quantum computation with a neutral atom processor
Ben W. Reichardt, Adam Paetznick, David Aasen +69
Quantum computing experiments are transitioning from running on physical qubits to using encoded, logical qubits. Fault-tolerant computation can identify and correct errors, and ha…
Demonstration of logical qubits and repeated error correction with better-than-physical error rates
A. Paetznick, M. P. da Silva, C. Ryan-Anderson +29
The promise of quantum computers hinges on the ability to scale to large system sizes, e.g., to run quantum computations consisting of more than 100 million operations fault-tolera…
Micro-Macro Consistency in Multiscale Modeling: Score-Based Model Assisted Sampling of Fast/Slow Dynamical Systems
Ellis R. Crabtree, Juan M. Bello-Rivas, Ioannis G. Kevrekidis
A valuable step in the modeling of multiscale dynamical systems in fields such as computational chemistry, biology, materials science and more, is the representative sampling of th…
Tipping Points of Evolving Epidemiological Networks: Machine Learning-Assisted, Data-Driven Effective Modeling
Nikolaos Evangelou, Tianqi Cui, Juan M. Bello-Rivas +2
We study the tipping point collective dynamics of an adaptive susceptible-infected-susceptible (SIS) epidemiological network in a data-driven, machine learning-assisted manner. We…
Locating saddle points using gradient extremals on manifolds adaptively revealed as point clouds
A. Georgiou, H. Vandecasteele, J. M. Bello-Rivas +1
Steady states are invaluable in the study of dynamical systems. High-dimensional dynamical systems, due to a separation of time-scales, often evolve towards a lower dimensional man…
Tasks Makyth Models: Machine Learning Assisted Surrogates for Tipping Points
Gianluca Fabiani, Nikolaos Evangelou, Tianqi Cui +4
We present a machine learning (ML)-assisted framework bridging manifold learning, neural networks, Gaussian processes, and Equation-Free multiscale modeling, for (a) detecting tipp…
Identifying Equivalent Training Dynamics
William T. Redman, Juan M. Bello-Rivas, Maria Fonoberova +3
Study of the nonlinear evolution deep neural network (DNN) parameters undergo during training has uncovered regimes of distinct dynamical behavior. While a detailed understanding o…
Gentlest ascent dynamics on manifolds defined by adaptively sampled point-clouds
Juan M. Bello-Rivas, Anastasia Georgiou, Hannes Vandecasteele +1
Finding saddle points of dynamical systems is an important problem in practical applications such as the study of rare events of molecular systems. Gentlest ascent dynamics (GAD) i…
Data-driven Discovery of Chemotactic Migration of Bacteria via Machine Learning
Yorgos M. Psarellis, Seungjoon Lee, Tapomoy Bhattacharjee +3
E. coli chemotactic motion in the presence of a chemoattractant field has been extensively studied using wet laboratory experiments, stochastic computational models as well as part…
GANs and Closures: Micro-Macro Consistency in Multiscale Modeling
Ellis R. Crabtree, Juan M. Bello-Rivas, Andrew L. Ferguson +1
Sampling the phase space of molecular systems -- and, more generally, of complex systems effectively modeled by stochastic differential equations -- is a crucial modeling step in m…
Learning Effective SDEs from Brownian Dynamics Simulations of Colloidal Particles
Nikolaos Evangelou, Felix Dietrich, Juan M. Bello-Rivas +4
We construct a reduced, data-driven, parameter dependent effective Stochastic Differential Equation (eSDE) for electric-field mediated colloidal crystallization using data obtained…
Staying the course: Locating equilibria of dynamical systems on Riemannian manifolds defined by point-clouds
Juan M. Bello-Rivas, Anastasia Georgiou, John Guckenheimer +1
We introduce a method to successively locate equilibria (steady states) of dynamical systems on Riemannian manifolds. The manifolds need not be characterized by an a priori known a…
On the Correspondence between Gaussian Processes and Geometric Harmonics
Felix Dietrich, Juan M. Bello-Rivas, Ioannis G. Kevrekidis
We discuss the correspondence between Gaussian process regression and Geometric Harmonics, two similar kernel-based methods that are typically used in different contexts. Research…
Manifold learning for parameter reduction
Alexander Holiday, Mahdi Kooshkbaghi, Juan M. Bello-Rivas +3
Large scale dynamical systems (e.g. many nonlinear coupled differential equations) can often be summarized in terms of only a few state variables (a few equations), a trait that re…
A mathematical framework for exact milestoning
David Aristoff, Juan M. Bello-Rivas, Ron Elber
We give a mathematical framework for Exact Milestoning, a recently introduced algorithm for mapping a continuous time stochastic process into a Markov chain or semi-Markov process…