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Quantum Computation of Stopping power for Inertial Fusion Target Design
Dominic Berry
Alina Kononov
Alec White
Joonho Lee
Andrew Baczewski
Proceedings of the National Academy of Sciences, 121(2024), e2317772121
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Stopping power is the rate at which a material absorbs the kinetic energy of a charged particle passing through it - one of many properties needed over a wide range of thermodynamic conditions in modeling inertial fusion implosions. First-principles stopping calculations are classically challenging because they involve the dynamics of large electronic systems far from equilibrium, with accuracies that are particularly difficult to constrain and assess in the warm-dense conditions preceding ignition. Here, we describe a protocol for using a fault-tolerant quantum computer to calculate stopping power from a first-quantized representation of the electrons and projectile. Our approach builds upon the electronic structure block encodings of Su et al. [PRX Quantum 2, 040332 2021], adapting and optimizing those algorithms to estimate observables of interest from the non-Born-Oppenheimer dynamics of multiple particle species at finite temperature. We also work out the constant factors associated with a novel implementation of a high order Trotter approach to simulating a grid representation of these systems. Ultimately, we report logical qubit requirements and leading-order Toffoli costs for computing the stopping power of various projectile/target combinations relevant to interpreting and designing inertial fusion experiments. We estimate that scientifically interesting and classically intractable stopping power calculations can be quantum simulated with
roughly the same number of logical qubits and about one hundred times more Toffoli gates than is required for state-of-the-art quantum simulations of industrially relevant molecules such as FeMoCo or P450.
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Floods are one of the most common natural disasters, with a disproportionate impact in developing countries that often lack dense streamflow gauge networks. Accurate and timely warnings are critical for mitigating flood risks, but hydrological simulation models typically must be calibrated to long data records in each watershed. Here we show that AI-based forecasting achieves reliability in predicting extreme riverine events in ungauged watersheds at up to a 5-day lead time that is similar to or better than the reliability of nowcasts (0-day lead time) from a current state of the art global modeling system (the Copernicus Emergency Management Service Global Flood Awareness System). Additionally, we achieve accuracies over 5-year return period events that are similar to or better than current accuracies over 1-year return period events. This means that AI can provide flood warnings earlier and over larger and more impactful events in ungauged basins. The model developed in this paper was incorporated into an operational early warning system that produces publicly available (free and open) forecasts in real time in over 80 countries. This work highlights a need for increasing the availability of hydrological data to continue to improve global access to reliable flood warnings.
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A scalable system to measure contrail formation on a per-flight basis
Erica Brand
Sebastian Eastham
Carl Elkin
Thomas Dean
Zebediah Engberg
Ulrike Hager
Joe Ng
Dinesh Sanekommu
Tharun Sankar
Marc Shapiro
Environmental Research Communications(2024)
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In this work we describe a scalable, automated system to determine from satellite data whether a given flight has made a persistent contrail.
The system works by comparing flight segments to contrails detected by a computer vision algorithm running on images from the GOES-16 Advanced Baseline Imager. We develop a `flight matching' algorithm and use it to label each flight segment as a `match' or `non-match'. We perform this analysis on 1.6 million flight segments and compare these labels to existing contrail prediction methods based on weather forecast data. The result is an analysis of which flights make persistent contrails several orders of magnitude larger than any previous work. We find that current contrail prediction models fail to correctly predict whether we will match a contrail in many cases.
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This is an invited OFC 2024 conference workshop talk regarding a new type of lower-power datacenter optics design choice: linear pluggable optics. In this talk I will discuss the fundamental performance constraints facing linear pluggable optics and their implications on DCN and ML use cases
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Dynamics of magnetization at infinite temperature in a Heisenberg spin chain
Trond Andersen
Rhine Samajdar
Andre Petukhov
Jesse Hoke
Dmitry Abanin
ILYA Drozdov
Xiao Mi
Alexis Morvan
Charles Neill
Rajeev Acharya
Richard Ross Allen
Kyle Anderson
Markus Ansmann
Frank Arute
Kunal Arya
Juan Atalaya
Gina Bortoli
Alexandre Bourassa
Leon Brill
Michael Broughton
Bob Buckley
Tim Burger
Nicholas Bushnell
Juan Campero
Hung-Shen Chang
Jimmy Chen
Benjamin Chiaro
Desmond Chik
Josh Cogan
Roberto Collins
Paul Conner
William Courtney
Alex Crook
Ben Curtin
Agustin Di Paolo
Andrew Dunsworth
Clint Earle
Lara Faoro
Edward Farhi
Reza Fatemi
Vinicius Ferreira
Ebrahim Forati
Austin Fowler
Brooks Foxen
Gonzalo Garcia
Élie Genois
William Giang
Dar Gilboa
Raja Gosula
Alejo Grajales Dau
Steve Habegger
Michael Hamilton
Monica Hansen
Sean Harrington
Paula Heu
Gordon Hill
Trent Huang
Ashley Huff
Bill Huggins
Sergei Isakov
Justin Iveland
Zhang Jiang
Cody Jones
Pavol Juhas
Mostafa Khezri
Marika Kieferova
Alexei Kitaev
Andrey Klots
Alexander Korotkov
Fedor Kostritsa
John Mark Kreikebaum
Dave Landhuis
Pavel Laptev
Kim Ming Lau
Lily Laws
Joonho Lee
Kenny Lee
Yuri Lensky
Alexander Lill
Wayne Liu
Salvatore Mandra
Orion Martin
Steven Martin
Seneca Meeks
Amanda Mieszala
Shirin Montazeri
Ramis Movassagh
Wojtek Mruczkiewicz
Ani Nersisyan
Michael Newman
JiunHow Ng
Murray Ich Nguyen
Tom O'Brien
Seun Omonije
Alex Opremcak
Rebecca Potter
Leonid Pryadko
David Rhodes
Charles Rocque
Negar Saei
Kannan Sankaragomathi
Henry Schurkus
Christopher Schuster
Mike Shearn
Aaron Shorter
Noah Shutty
Vladimir Shvarts
Vlad Sivak
Jindra Skruzny
Clarke Smith
Rolando Somma
George Sterling
Doug Strain
Marco Szalay
Doug Thor
Alfredo Torres
Guifre Vidal
Benjamin Villalonga
Cheng Xing
Jamie Yao
Ping Yeh
Juhwan Yoo
Grayson Young
Yaxing Zhang
Ningfeng Zhu
Jeremy Hilton
Anthony Megrant
Yu Chen
Vadim Smelyanskiy
Vedika Khemani
Sarang Gopalakrishnan
Tomaž Prosen
Science, 384(2024), pp. 48-53
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Understanding universal aspects of quantum dynamics is an unresolved problem in statistical mechanics. In particular, the spin dynamics of the one-dimensional Heisenberg model were conjectured as to belong to the Kardar-Parisi-Zhang (KPZ) universality class based on the scaling of the infinite-temperature spin-spin correlation function. In a chain of 46 superconducting qubits, we studied the probability distribution of the magnetization transferred across the chain’s center, P(M). The first two moments of P(M) show superdiffusive behavior, a hallmark of KPZ universality. However, the third and fourth moments ruled out the KPZ conjecture and allow for evaluating other theories. Our results highlight the importance of studying higher moments in determining dynamic universality classes and provide insights into universal behavior in quantum systems.
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Stable quantum-correlated many-body states through engineered dissipation
Xiao Mi
Alexios Michailidis
Sara Shabani
Jerome Lloyd
Rajeev Acharya
Igor Aleiner
Trond Andersen
Markus Ansmann
Frank Arute
Kunal Arya
Juan Atalaya
Gina Bortoli
Alexandre Bourassa
Leon Brill
Michael Broughton
Bob Buckley
Tim Burger
Nicholas Bushnell
Jimmy Chen
Benjamin Chiaro
Desmond Chik
Charina Chou
Josh Cogan
Roberto Collins
Paul Conner
William Courtney
Alex Crook
Ben Curtin
Alejo Grajales Dau
Dripto Debroy
Agustin Di Paolo
ILYA Drozdov
Andrew Dunsworth
Lara Faoro
Edward Farhi
Reza Fatemi
Vinicius Ferreira
Ebrahim Forati
Austin Fowler
Brooks Foxen
Élie Genois
William Giang
Dar Gilboa
Raja Gosula
Steve Habegger
Michael Hamilton
Monica Hansen
Sean Harrington
Paula Heu
Trent Huang
Ashley Huff
Bill Huggins
Sergei Isakov
Justin Iveland
Zhang Jiang
Cody Jones
Pavol Juhas
Kostyantyn Kechedzhi
Mostafa Khezri
Marika Kieferova
Alexei Kitaev
Andrey Klots
Alexander Korotkov
Fedor Kostritsa
John Mark Kreikebaum
Dave Landhuis
Pavel Laptev
Kim Ming Lau
Lily Laws
Joonho Lee
Kenny Lee
Yuri Lensky
Alexander Lill
Wayne Liu
Orion Martin
Amanda Mieszala
Shirin Montazeri
Alexis Morvan
Ramis Movassagh
Wojtek Mruczkiewicz
Charles Neill
Ani Nersisyan
Michael Newman
JiunHow Ng
Murray Ich Nguyen
Tom O'Brien
Alex Opremcak
Andre Petukhov
Rebecca Potter
Leonid Pryadko
Charles Rocque
Negar Saei
Kannan Sankaragomathi
Henry Schurkus
Christopher Schuster
Mike Shearn
Aaron Shorter
Noah Shutty
Vladimir Shvarts
Jindra Skruzny
Clarke Smith
Rolando Somma
George Sterling
Doug Strain
Marco Szalay
Alfredo Torres
Guifre Vidal
Benjamin Villalonga
Cheng Xing
Jamie Yao
Ping Yeh
Juhwan Yoo
Grayson Young
Yaxing Zhang
Ningfeng Zhu
Jeremy Hilton
Anthony Megrant
Yu Chen
Vadim Smelyanskiy
Dmitry Abanin
Science, 383(2024), pp. 1332-1337
Preview abstract
Engineered dissipative reservoirs have the potential to steer many-body quantum systems toward correlated steady states useful for quantum simulation of high-temperature superconductivity or quantum magnetism. Using up to 49 superconducting qubits, we prepared low-energy states of the transverse-field Ising model through coupling to dissipative auxiliary qubits. In one dimension, we observed long-range quantum correlations and a ground-state fidelity of 0.86 for 18 qubits at the critical point. In two dimensions, we found mutual information that extends beyond nearest neighbors. Lastly, by coupling the system to auxiliaries emulating reservoirs with different chemical potentials, we explored transport in the quantum Heisenberg model. Our results establish engineered dissipation as a scalable alternative to unitary evolution for preparing entangled many-body states on noisy quantum processors.
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SEEDS: Emulation of Weather Forecast Ensembles with Diffusion Models
Fei Sha
John Anderson
Science Advances, 10(2024), eadk4489
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Probabilistic forecasting is crucial to decision-making under uncertainty about future weather. The dominant approach is to use an ensemble of forecasts to represent and quantify uncertainty in operational numerical weather prediction. However, generating ensembles is computationally costly. In this paper, we propose to generate ensemble forecasts at scale by leveraging recent advances in generative artificial intelligence. Our approach learns a data-driven probabilistic diffusion model from the 5-member ensemble GEFS reforecast dataset. The model can then be sampled efficiently to produce realistic weather forecasts, conditioned on a few members of the operational GEFS forecasting system. The generated ensembles have similar predictive skill as the full GEFS 31-member ensemble, evaluated against ERA5 reanalysis, and emulate well the statistics of large physics-based ensembles. We also apply the same methodology to developing a diffusion model for generative post-processing: the model directly learns to correct biases present in the emulated forecasting system by leveraging reanalysis data as labels during training. Ensembles from this generative post-processing model show greater reliability and accuracy, particularly in extreme event classification. In general, they are more reliable and forecast the probability of extreme weather more accurately than the GEFS operational ensemble. Our models achieve these results at less than 1/10th of the computational cost incurred by the operational GEFS system.
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Scientists’ Perspectives on the Potential for Generative AI in their Fields
Meredith Ringel Morris
arXiv(2023)
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Generative AI models, including large language models and multimodal models that include text and other media, are on the cusp of transforming many aspects of modern life, including entertainment, education, civic life, the arts, and a range of professions. There is potential for Generative AI to have a substantive impact on the methods and pace of discovery for a range of scientific disciplines. We interviewed twenty scientists from a range of fields (including the physical, life, and social sciences) to gain insight into whether or how Generative AI technologies might add value to the practice of their respective disciplines, including not only ways in which AI might accelerate scientific discovery (i.e., research), but also other aspects of their profession, including the education of future scholars and the communication of scientific findings. In addition to identifying opportunities for Generative AI to augment scientists’ current practices, we also asked participants to reflect on concerns about AI. These findings can help guide the responsible development of models and interfaces for scientific education, inquiry, and communication.
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Preview abstract
With modern fluorescent probes and light microscopes, the possibilities of monitoring the dynamics of cells, organelles, molecular complexes and even single molecules with high spatiotemporal resolution are more than ever [1]. Characterizing the motion of cellular and molecular entities can reveal a great deal of information about their functions and interactions and overall activity landscape [2]. Motion characterization is generally the end product of an image analysis pipeline that starts with identifying and localizing the objects in each image (segmentation/detection), tracking them over time, and then analyzing the resulting trajectories to characterize the objects’ motion. Whether objects are large (e.g. cells or organelles) or small (e.g. molecules or molecular complexes), as long as they can be represented by coordinates (e.g. cell centroid position or molecular position), following them over time in a series of images is effectively a (multiple) particle tracking problem. In their recent publication in Nature Machine Intelligence, Pineda et al. [3] describe a powerful deep learning approach based on graph neural networks (GNNs) for particle tracking or, if desired, for the characterization of object motion without explicit tracking.
Particle tracking is often the most challenging step in the analysis pipeline from images to motion characterization. Whenever the analysis involves a relatively high density of heterogeneously moving objects, there is ambiguity in determining which object has gonewent where throughout the image series [4]. In addition, objects may merge with each other – due to crossing paths or interactions – and may undergo splitting, such as during cell division. Despite these challenges, a high density of tracked objects is often desired, because of the rich information that it yields about the system studied [5]. The novel GNN-based approach by Pineda et al. [3], named MAGIK, offers solutions to the tracking problem in two ways: First, MAGIK can be employed to construct the trajectories of the imaged objects from the graph of their connections in space and time. Second, MAGIK can be employed to characterize the motion of the imaged objects directly from their graph of spatiotemporal connections, without explicit tracking.
Graphs are ubiquitously used in science to represent complex systems of interacting objects, from molecules to social and transportation networks [6]. GNNs provide a framework for incorporating existing information about the objects, with an inductive bias based on a larger structure relating them, to make predictions about these objects or the system as a whole. In MAGIK [3], spatiotemporal connections between imaged objects, encoded in the structure of the graph, provide this inductive bias , with the premise that objects close in space-time are likely to be the same. MAGIK utilizes this graph representation in a powerful way by employing GNNs [7] to perform various tracking and motion characterization tasks. The GNN model proposed in MAGIK considers both spatial and temporal information in a static graph. This model is enhanced by an adaptive and interpretable attention mechanism. Attention estimates the strength of association among the objects and provides insights into the dynamics of the system for the task.
GNNs enable MAGIK to provide a versatile platform for performing multiple tasks from linking coordinates into trajectories to inferring local and global dynamic properties. MAGIK is tested for its flexibility and reliability in real and simulated scenarios corresponding to a variety of biological experiments. The results of the tests show that MAGIK is able to identify which spatiotemporal connections in a graph influence the dynamic properties of each object. They further show that MAGIK accurately constructs trajectories, obtaining outstanding results for cell tracking, including the identification of cell division events, using multiple microscopy techniques and cell types. As in most applications the final goal of tracking is to characterize the dynamics of the system, Pineda et al. [3] have tested MAGIK for quantifying motion parameters without explicit tracking, and they have shown that MAGIK can accurately and sensitively quantify local or global motion properties of the imaged objects. Technically, MAGIK performs these various tasks by tailoring its training to the task: Tracking as a graph edge classification task, local motion motion characterization as a graph node regression task, and global motion characterization as a graph-level regression or classification task.
As demonstrated by MAGIK, GNNs offer powerful tools for the analysis of the spatiotemporal connections between objects in biological images. New developments in the fields of graphs and GNNs will further advance this goal. One possibility is to replace the fixed graph and the fully connected graph in MAGIK with a learnable sparse graph [8]. Another possibility is to use hypergraphs, which go beyond binary connections (a fundamental limitation of graphs). This would be a promising approach to characterize the spatiotemporal connections of systems with complex interactions [9]. Furthermore, as the problem studied here is temporal in nature, it may benefit from temporal GNNs [10], which directly incorporate time into the GNN formulation. All in all, the powerful combination of cutting-edge microscopes, fluorescent probes and geometric deep learning analytical tools will aid with the study of the organization, dynamics and interactions of diverse systems, from molecules in a cell, to cells in a tissue, and beyond.
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Estimates of broadband upwelling irradiance from GOES-16 ABI
Sixing Chen
Vincent Rudolf Meijer
Joe Ng
Geoff Davis
Carl Elkin
Remote Sensing of Environment, 285(2023)
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Satellite-derived estimates of the Earth’s radiation budget are crucial for understanding and predicting the weather and climate. However, existing satellite products measuring broadband outgoing longwave radiation (OLR) and reflected shortwave radiation (RSR) have spatio-temporal resolutions that are too coarse to evaluate important radiative forcers like aircraft condensation trails. We present a neural network which estimates OLR and RSR based on narrowband radiances, using collocated Cloud and Earth’s Radiant Energy System (CERES) and GOES-16 Advanced Baseline Imager (ABI) data. The resulting estimates feature strong agreement with the CERES data products (R^2 = 0.977 for OLR and 0.974 for RSR on CERES Level 2 footprints), and we provide open access to the collocated satellite data and model outputs on all available GOES-16 ABI data for the 4 years from 2018–2021.
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Single-cell gene regulatory network prediction by explainable AI
Philipp Keyl
Philip Bischoff
Gabriel Dernbach
Michael Bockmayr
Rebecca Fritz
David Horst
Nils Blüthgen
Grégoire Montavon
Frederick Klauschen
Nucleic Acids Research(2023), gkac1212
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The molecular heterogeneity of cancer cells contributes to the often partial response to targeted therapies and relapse of disease due to the escape of resistant cell populations. While single-cell sequencing has started to improve our understanding of this heterogeneity, it offers a mostly descriptive view on cellular types and states. To obtain more functional insights, we propose scGeneRAI, an explainable deep learning approach that uses layer-wise relevance propagation (LRP) to infer gene regulatory networks from static single-cell RNA sequencing data for individual cells. We benchmark our method with synthetic data and apply it to single-cell RNA sequencing data of a cohort of human lung cancers. From the predicted single-cell networks our approach reveals characteristic network patterns for tumor cells and normal epithelial cells and identifies subnetworks that are observed only in (subgroups of) tumor cells of certain patients. While current state-of-the-art methods are limited by their ability to only predict average networks for cell populations, our approach facilitates the reconstruction of networks down to the level of single cells which can be utilized to characterize the heterogeneity of gene regulation within and across tumors.
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Chimane-Mosetén
Jeanette Sakel
Amazonian Languages: An International Handbook, De Gruyter Mouton(2023)
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Chimane-Mosetén (also known as Mosetenan; ISO 639–3: cas; Glottocode: mose1249) is a dialect continuum spoken by 13,500–16,000 people in the Amazonian region of northern Bolivia. It has not been convincingly shown to be related to any other language. Its status as an isolate makes it unique in many respects, not least in its combination of features typical of both Amazonian and Andean languages. Like its closer geographical neighbors in Amazonian Bolivia, including Movima, Tacana, Reyesano, and Cavineña, it exhibits contrastive nasality in the vowel system and is head marking and predominantly agglutinative. Bound pronominal forms marking arguments in the clause have the same form as bound pronominals marking possessors. Subordinate clauses typically involve nominalized verbs. Unlike most of its Amazonian neighbors, on the other hand, it does not have a semantically-based classifier or gender system but instead features arbitrarily assigned masculine or feminine gender. It also does not feature any incorporation of nouns, adverbs, or adpositions. It has an extensive oblique case-marking system, though core case-marking does not occur. More similar to Quechua and other Andean languages, it features a complex predicate-argument agreement system in which one or more agreement suffixes cross-reference the subject and object arguments of a transitive verb. It also has a large class of lexical numbers following a decimal numeral system.
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Accelerating Molecular Graph Neural Networks via Knowledge Distillation
Filip Ekström Kelvinius
Dimitar Georgiev
Artur Petrov Toshev
Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS)(2023)
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Recent advances in graph neural networks (GNNs) have allowed molecular simulations with accuracy on par with conventional gold-standard methods at a fraction of the computational cost. Nonetheless, as the field has been progressing to bigger and more complex architectures, state-of-the-art GNNs have become largely prohibitive for many large-scale applications. In this paper, we, for the first time, explore the utility of knowledge distillation (KD) for accelerating molecular GNNs. To this end, we devise KD strategies that facilitate the distillation of hidden representations in directional and equivariant GNNs and evaluate their performance on the regression task of energy and force prediction. We validate our protocols across different teacher-student configurations and demonstrate that they can boost the predictive accuracy of student models without altering their architecture. We also conduct comprehensive optimization of various components of our framework, and investigate the potential of data augmentation to further enhance performance. All in all, we manage to close as much as 59% of the gap in predictive accuracy between models like GemNet-OC and PaiNN with zero additional cost at inference.
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Evolve Smoothly, Fit Consistently: Learning Smooth Latent Dynamics For Advection-Dominated Systems
Anudhyan Boral
Fei Sha
International Conference on Learning Representations(2023) (to appear)
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We present a data-driven, space-time continuous framework to learn surrogate models for complex physical systems described by advection-dominated partial differential equations. Those systems have slow-decaying Kolmogorov n-width that hinders standard methods, including reduced order modeling, from producing high-fidelity simulations at low cost. In this work, we construct hypernetwork-based latent dynamical models directly on the parameter space of a compact representation network. We leverage the expressive power of the network and a specially designed consistency-inducing regularization to obtain latent trajectories that are both low-dimensional and smooth. These properties render our surrogate models highly efficient at inference time. We show the efficacy of our framework by learning models that generate accurate multi-step rollout predictions at much faster inference speed compared to competitors, for several challenging examples.
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How Climate Change was Won
Communications of the ACM, Vol. 66 No. 11(2023), Pages 100-ff
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A short fictional piece telling the story of how climate change was won, from the perspective of a newscaster on Mars.
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