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.

Learn more

Highlighted projects

Scaling Up Fundamental Quantum Chemistry Simulations on Quantum Hardware

Accurate computational prediction of chemical processes from the quantum mechanical laws that govern them is a tool that can unlock new frontiers in chemistry, improving a wide variety of industries.

Announcing OpenFermion: The Open Source Chemistry Package for Quantum Computers

OpenFermion is a library for simulating the systems of interacting electrons (fermions) which give rise to the properties of matter.

Towards an exact (quantum) description of chemistry

The goal of our experiment was to use quantum hardware to efficiently solve the molecular electronic structure problem, which seeks the solution for the lowest energy configuration of electrons in the presence of a given nuclear configuration.

Featured publications

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...

View details

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...

View details

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...

View details

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...

View details

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....

View details

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

ICML workshop on Climate Change 2021 (2021)

Machine learning accelerated computational fluid dynamics

Numerical simulation of fluids plays an essential role in modeling many physical phenomena, such as in weather, climate, aerodynamics and plasma physics. Fluids are well described by the Navier-Stokes equations, but solving these equations at scale remains daunting, limited by the computational cost of resolving the smallest spatiotemporal features. This leads to unfavorable trade-offs between...

View details

Machine learning accelerated computational fluid dynamics

Ayya Alieva, Dmitrii Kochkov, Jamie Alexander Smith, Michael Brenner, Qing Wang, Stephan Hoyer

Proceedings of the National Academy of Sciences USA (2021)

Kohn-Sham equations as regularizer: building prior knowledge into machine-learned physics

Including prior knowledge is important for effective machine learning models in physics and is usually achieved by explicitly adding loss terms or constraints on model architectures. Prior knowledge embedded in the physics computation itself rarely draws attention. We show that solving the Kohn-Sham equations when training neural networks for the exchange-correlation functional provides an...

View details

Kohn-Sham equations as regularizer: building prior knowledge into machine-learned physics

Li Li, Stephan Hoyer, Ryan Pederson, Ruoxi Sun, Ekin Dogus Cubuk, Patrick Francis Riley, Kieron Burke

Phys. Rev. Lett., vol. 126 (2021), pp. 036401

Exponential suppression of bit or phase flip errors with repetitive quantum error correction

Realizing the potential of quantum computing will require achieving sufficiently low logical error rates. Many applications call for error rates below 10^-15, but state-of-the-art quantum platforms typically have physical error rates near 10^-3. Quantum error correction (QEC) promises to bridge this divide by distributing quantum logical information across many physical qubits so that errors...

View details

Exponential suppression of bit or phase flip errors with repetitive quantum error correction

Adam Jozef Zalcman, Alan Derk, Alan Ho, Alex Opremcak, Alexander Korotkov, Alexandre Bourassa, Andre Gregory Petukhov, Andreas Bengtsson, Andrew Dunsworth, Anthony Megrant, Austin Fowler, Bálint Pató, Benjamin Chiaro, Benjamin Villalonga, Brian Burkett, Brooks Riley Foxen, Catherine Erickson, Charles Neill, Chris Quintana, Cody Jones, Craig Michael Gidney, Daniel Eppens, Daniel Sank, Dave Landhuis, David A Buell, Doug Strain, Dvir Kafri, Edward Farhi, Eric Ostby, Erik Lucero, Evan Jeffrey, Fedor Kostritsa, Frank Carlton Arute, Hartmut Neven, Igor Aleiner, Jamie Yao, Jarrod Ryan McClean, Jeremy Patterson Hilton, Jimmy Chen, Jonathan Arthur Gross, Joseph Bardin, Josh Mutus, Juan Atalaya, Julian Kelly, Kevin Miao, Kevin Satzinger, Kostyantyn Kechedzhi, Kunal Arya, Marco Szalay, Marissa Giustina, Masoud Mohseni, Matt McEwen, Matt Trevithick, Matthew Neeley, Matthew P Harrigan, Michael Broughton, Michael Newman, Murphy Yuezhen Niu, Nicholas Bushnell, Nicholas Redd, Nicholas Rubin, Ofer Naaman, Orion Martin, Paul Victor Klimov, Pavel Laptev, Pedram Roushan, Ping Yeh, Rami Barends, Roberto Collins, Ryan Babbush, Sabrina Hong, Sean Demura, Sean Harrington, Seon Kim, Sergei Isakov, Sergio Boixo, Ted White, Thomas E O'Brien, Trent Huang, Trevor Mccourt, Vadim Smelyanskiy, Vladimir Shvarts, William Courtney, Wojtek Mruczkiewicz, Xiao Mi, Yu Chen, Zhang Jiang

Nature (2021)

GAN-Mediated Batch Equalization

Advances in automation and imaging have made it possible to capture large imagedatasets for experiments that span multiple weeks (i.e. batches). However, almostall images experience batch-to-batch variation due to uncontrollable noise (e.g.different stain intensity or illumination conditions), and such complication makesit difficult to make biological comparison across all of the conditions...

View details

GAN-Mediated Batch Equalization

Arun Narayanaswamy, Cassandra Xia, Mike Ando, Subhashini Venugopalan, Wesley Qian

bioRxiv (2020)

Accurately computing electronic properties of materials using eigenenergies

A promising approach to study quantum materials is to simulate them on an engineered quantum platform. However, achieving the accuracy needed to outperform classical methods has been an outstanding challenge. Here, using superconducting qubits, we provide an experimental blueprint for a programmable and accurate quantum matter simulator and demonstrate how to probe fundamental electronic...

View details

Accurately computing electronic properties of materials using eigenenergies

Adam Jozef Zalcman, Alan Derk, Alan Ho, Alex Opremcak, Alexander Korotkov, Andre Gregory Petukhov, Andreas Bengtsson, Andrew Dunsworth, Anthony Megrant, Austin Fowler, Bálint Pató, Benjamin Chiaro, Benjamin Villalonga, Bob Benjamin Buckley, Brian Burkett, Brooks Riley Foxen, Catherine Erickson, Charles Neill, Chris Quintana, Cody Jones, Craig Michael Gidney, Daniel Eppens, Daniel Sank, Dave Landhuis, David A Buell, Doug Strain, Dvir Kafri, Edward Farhi, Eric Ostby, Erik Lucero, Evan Jeffrey, Fedor Kostritsa, Frank Carlton Arute, Hartmut Neven, Igor Aleiner, Jamie Yao, Jarrod Ryan McClean, Jeremy Patterson Hilton, Jimmy Chen, Jonathan Arthur Gross, Joseph Bardin, Josh Mutus, Juan Atalaya, Juan Campero, Julian Kelly, Kevin Miao, Kevin Satzinger, Kostyantyn Kechedzhi, Kunal Arya, Lev Ioffe, Marco Szalay, Marissa Giustina, Masoud Mohseni, Matt Jacob-Mitos, Matt McEwen, Matt Trevithick, Matthew Neeley, Matthew P Harrigan, Michael Blythe Broughton, Michael Newman, Murphy Yuezhen Niu, Nicholas Bushnell, Nicholas Redd, Nicholas Rubin, Ofer Naaman, Orion Martin, Paul Victor Klimov, Pavel Laptev, Pedram Roushan, Ping Yeh, Rami Barends, Roberto Collins, Ryan Babbush, Sabrina Hong, Sean Demura, Sean Harrington, Seon Kim, Sergei Isakov, Sergio Boixo, Ted White, Thomas E O'Brien, Trent Huang, Trevor Mccourt, Vadim Smelyanskiy, Vladimir Shvarts, William Courtney, William J. Huggins, Wojtek Mruczkiewicz, Xiao Mi, Yu Chen, Zhang Jiang

arXiv preprint arXiv:2012.00921 (2020)

Some of our locations

Cambridge

San Francisco Bay Area

Seattle-Kirkland

India

Some of our people

John C. Platt

Michael Frumkin

Jason Miller

Hartmut Neven

Lucy Colwell

Marissa Giustina

Varun Gulshan

Michelle Dimon

Michael P Brenner

Abe Asfaw

Christopher H Van Arsdale

Subhashini Venugopalan

Join us

We're looking for talented people to join our team

See opportunities