Applied science

Combining computer science with physics and biology to create breakthroughs that help the world.

About the team

Computer science and natural science are complementary: breakthroughs in one can lead to remarkable advances in the other. The goal of the Applied Science organization at Google is to cross-fertilize these two fields. There are four main efforts in Applied Science: Quantum Computing, Google Accelerated Science, Climate and Energy, and Scientific Computing Tools.

Quantum Computing uses advances in applied physics to push the state-of-the-art in computation. Google Accelerated Science and Climate and Energy do the opposite: they use the latest advances in machine learning and artificial intelligence to accelerate progress in natural sciences, including societally-important areas such as biomedical research and zero-carbon energy sources. Finally, we supply Scientific Computing Tools such as Colab to many internal groups to enhance their data and machine learning productivity.

Research areas

Algorithms and theory

General science

Health & bioscience

Machine intelligence

Machine perception

Quantum computing

Team focus summaries

Climate and energy

The Climate & Energy team is exploring how to use large-scale computing and machine intelligence to diminish or avoid climate disruption. We partner with fusion companies to accelerate the progress of commercially viable fusion, model techno-economic scenarios for decarbonization, research new techniques for carbon sequestration, and look for novel ways to improve our understanding of earth's ecosystem response to climate change.

Physics

The physics team seeks to combine advances in machine learning and other Google technologies to advance our understanding of the physical world. Current projects range from machine learning for scientific computing, developing efficient algorithms for solving nonlinear partial differential equations, applying differentiable algorithms to interpret microscopy data, enabling microscopy on phones, and building algorithms for understanding dysarthric speech.

Quantum AI

The Quantum AI Lab is building quantum processors and algorithms to dramatically accelerate computational tasks for machine intelligence. We are developing quantum algorithms with a particular focus on those which can already run on today’s pre-error corrected quantum processors. Quantum algorithms for optimization, sampling, and quantum simulation hold the promise of dramatic speedups over the fastest classical computers.

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Highlighted projects

Project Contrails

Leveraging computer vision to mitigate aviation’s climate impact.

Suppressing quantum errors by scaling a surface code logical qubit

Large-scale quantum computer development requires qubits with much lower error rates. Our latest results demonstrate a logical qubit prototype, a milestone on the path toward scalable fault-tolerant quantum computing.

TensorStore for High-Performance, Scalable Array Storage

TensorStore, a library designed for storage and manipulation of n-dimensional data, addresses key engineering challenges in scientific computing through better management and processing of large peta-scale datasets and creation of LLMs, such as PaLM.

Quantum Advantage in Learning from Experiments

Quantum technology promises to revolutionize how we learn about the physical world. Researchers have shown that quantum ML algorithms can perform exponentially better than classical learning algorithms at many tasks.

Featured publications

Exponential Quantum Speedup in Simulating Coupled Classical Oscillators

We present a quantum algorithm for simulating the classical dynamics of 2^n coupled oscillators (e.g., 2^n masses coupled by springs). Our approach leverages a mapping between the Schrodinger equation and Newton's equations for harmonic potentials such that the amplitudes of the evolved quantum state encode the momenta and displacements of the classical oscillators. When individual masses and...

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Exponential Quantum Speedup in Simulating Coupled Classical Oscillators

Ryan Babbush, Dominic Berry, Robin Kothari, Rolando Somma, Nathan Wiebe

arXiv:2303.13012 (2023)

Quantum Simulation of Exact Electron Dynamics can be more Efficient than Classical Mean-Field Methods

Quantum algorithms for simulating electronic ground states are slower than popular classical mean-field algorithms such as Hartree-Fock and density functional theory, but offer higher accuracy. Accordingly, quantum computers have been predominantly regarded as competitors to only the most accurate and costly classical methods for treating electron correlation. However, here we tighten bounds...

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Quantum Simulation of Exact Electron Dynamics can be more Efficient than Classical Mean-Field Methods

Ryan Babbush, William J. Huggins, Dominic W. Berry, Shu Fay Ung, Andrew Zhao, David Reichman, Hartmut Neven, Andrew Baczewski, Joonho Lee

Nature Communications, vol. 14 (2023), pp. 4058

Longitudinal fundus imaging and its genome-wide association analysis provides evidence for a human retinal aging clock

Background Biological age, distinct from an individual’s chronological age, has been studied extensively through predictive aging clocks. However, these clocks have limited accuracy in short time-scales. Deep learning approaches on imaging datasets of the eye have proven powerful for a variety of quantitative phenotype inference and provide an opportunity to explore organismal aging and tissue...

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Longitudinal fundus imaging and its genome-wide association analysis provides evidence for a human retinal aging clock

Sara Ahadi, Kenneth A Wilson Jr,, Boris Babenko, Cory McLean, Drew Bryant, Orion Pritchard, Ajay Kumar, Enrique M Carrera, Ricardo Lamy, Jay M Stewart, Avinash Varadarajan, Marc Berndl, Pankaj Kapahi, Ali Bashir

eLife (2023)

Estimates of broadband upwelling irradiance from GOES-16 ABI

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...

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Estimates of broadband upwelling irradiance from GOES-16 ABI

Kevin James Fleming McCloskey, Sixing Chen, Vincent Rudolf Meijer, Joe Ng, Geoff Davis, Carl Elkin, Christopher H Van Arsdale, Scott Davidson Geraedts

Remote Sensing of Environment, vol. 285 (2023)

Suppressing quantum errors by scaling a surface code logical qubit

Practical quantum computing will require error rates that are well below what is achievable with physical qubits. Quantum error correction [1, 2] offers a path to algorithmically-relevant error rates by encoding logical qubits within many physical qubits, where increasing the number of physical qubits enhances protection against physical errors. However, introducing more qubits also increases...

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Suppressing quantum errors by scaling a surface code logical qubit

Abe Asfaw, Anthony Megrant, Cody Jones, Craig Gidney, Dave Bacon, Dripto Debroy, Dvir Kafri, Erik Lucero, Hartmut Neven, Jeremy Hilton, Jimmy Chen, John Platt, Jonathan Gross, Juan Atalaya, Julian Kelly, Kenny Lee, Kevin Satzinger, Michael Newman, Sergio Boixo, Vadim Smelyanskiy, Yu Chen, Catherine Vollgraff Heidweiller

Nature (2023)

CO2 capture by pumping surface acidity to the deep ocean

The majority of IPCC scenarios call for active CO2 removal (CDR) to remain below 2ºC of warming. On geological timescales, ocean uptake regulates atmospheric CO2 concentration, with two homeostats driving sequestration: dissolution of deep ocean calcite deposits and terrestrial weathering of silicate rocks, acting on 1ka to 100ka timescales. Many current ocean-based CDR proposals effectively...

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CO2 capture by pumping surface acidity to the deep ocean

Christopher H Van Arsdale, John Platt, Mike Tyka

Energy and Environmental Science (EES) (2022)

Noise-resilient Majorana Edge Modes on a Chain of Superconducting Qubits

Inherent symmetry of a quantum system may protect its otherwise fragile states. Leveraging such protection requires testing its robustness against uncontrolled environmental interactions. Using 47 superconducting qubits, we implement the kicked Ising model which exhibits Majorana edge modes (MEMs) protected by a $\mathbb{Z}_2$-symmetry. Remarkably, we find that any multi-qubit Pauli operator...

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Noise-resilient Majorana Edge Modes on a Chain of Superconducting Qubits

Abe Asfaw, Adam Jozef Zalcman, Aditya Locharla, Alejandro Grajales Dau, Alex Crook, Alex Opremcak, Alexa Rubinov, Alexander Korotkov, Alexandre Bourassa, Alexei Kitaev, Alexis Morvan, Andre Gregory Petukhov, Andreas Bengtsson, Andrew Dunsworth, Andrey Klots, Anthony Megrant, Ashley Anne Huff, Austin Fowler, Benjamin Chiaro, Benjamin Villalonga, Bernardo Meurer Costa, Bob Benjamin Buckley, Brian Burkett, Brooks Foxen, Catherine Erickson, Catherine Vollgraff Heidweiller, Charles Neill, Chris Quintana, Christopher Schuster, Cody Jones, Craig Michael Gidney, Daniel Eppens, Daniel Sank, Dar Gilboa, Dave Bacon, Dave Landhuis, David A Buell, Dmitry Abanin, Doug Strain, Dripto M. Debroy, Dvir Kafri, Ebrahim Forati, Edward Farhi, Emily Mount, Erik Lucero, Evan Jeffrey, Fedor Kostritsa, Frank Carlton Arute, Guifre Vidal, Hartmut Neven, Igor Aleiner, Jamie Yao, Jarrod Ryan McClean, Jeremy Patterson Hilton, Joao Basso, Joe Bardin, John Mark Kreikebaum, Jonathan Arthur Gross, Joonho Lee, Juan Atalaya, Juhwan Yoo, Julian Kelly, Justin Thomas Iveland, Kannan Aryaperumal Sankaragomathi, Kenny Lee, Kevin Miao, Kevin Satzinger, Kim Ming Lau, Kostyantyn Kechedzhi, Kunal Arya, Lara Faoro, Leon Brill, Leslie Flores, Lev Ioffe, Marco Szalay, Marissa Giustina, Markus Rudolf Hoffmann, Masoud Mohseni, Matt McEwen, Matt P Harrigan, Matthew Neeley, Michael Blythe Broughton, Michael Newman, Michel Henri Devoret, Mike Shearn, Murphy Yuezhen Niu, Nicholas Bushnell, Nicholas Rubin, Ofer Naaman, Orion Martin, Paul Conner, Paul Victor Klimov, Pavel Laptev, Pedram Roushan, Ping Yeh, Rajeev Acharya, Rebecca Potter, Reza Fatemi, Roberto Collins, Ryan Babbush, Sabrina Hong, Sean Demura, Seon Kim, Sergei Isakov, Sergio Boixo, Shirin Montazeri, Steve Habegger, Tanuj Khattar, Ted White, Thomas E O'Brien, Trent Huang, Trond Ikdahl Andersen, Vadim Smelyanskiy, Vladimir Shvarts, Wayne Liu, William Courtney, William Giang, William J. Huggins, Wojtek Mruczkiewicz, Xiao Mi, Yaxing Zhang, Yu Chen, Yuan Su, Zhang Jiang, Zijun Chen

Science (2022) (to appear)

Next Day Wildfire Spread: A Machine Learning Dataset to Predict Wildfire Spreading From Remote-Sensing Data

Predicting wildfire spread is critical for land management and disaster preparedness. To this end, we present “Next Day Wildfire Spread,” a curated, large-scale, multivariate dataset of historical wildfires aggregating nearly a decade of remote-sensing data across the United States. In contrast to existing fire datasets based on Earth observation satellites, our dataset combines 2-D fire data...

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Next Day Wildfire Spread: A Machine Learning Dataset to Predict Wildfire Spreading From Remote-Sensing Data

Fantine Huot, Lily Hu, Nita Goyal, Tharun Pratap Sankar, Matthias Ihme, Yi-fan Chen

IEEE Transactions on Geoscience and Remote Sensing, vol. 60 (2022), pp. 1-13

Comprehensive Imaging of C-2W Plasmas: Instruments and Applications

The C-2W device (“Norman”), has produced and sustained beam-driven field-reversed configuration (FRC) plasmas embedded in a magnetic mirror geometry using neutral beams and end-bias electrodes located in expander divertors. Many discrete vessels comprise this device, and a suite of spatially and radiometrically calibrated, high-speed camera systems have been deployed to visualize the plasma...

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Comprehensive Imaging of C-2W Plasmas: Instruments and Applications

Erik Granstedt, Deepak Gupta, James Sweeney, Matthew Tobin, Michael Dikovsky, the TAE team

Review of Scientific Instruments, vol. 92 (2021), pp. 043515

A human-labeled Landsat contrails dataset

Contrails (condensation trails) are the ice clouds that trail behind aircraft as they fly through cold and moist regions of the atmosphere. Avoiding these regions could potentially be an inexpensive way to reduce over half of aviation's impact on global warming. Development and evaluation of these avoidance strategies greatly benefits from the ability to detect contrails on satellite imagery....

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A human-labeled Landsat contrails dataset

Kevin James Fleming McCloskey, Scott Davidson Geraedts, Brendan Henry Jackman, Vincent Rudolf Meijer, Erica Wickstrom Brand, David K. Fork, John Platt, Carl Elkin, Christopher H Van Arsdale