A research effort from Google AI that aims to build quantum processors and develop novel quantum algorithms to dramatically accelerate computational tasks for machine learning.
About the team
Google AI Quantum is advancing quantum computing by developing quantum processors and novel quantum algorithms to help researchers and developers solve near-term problems both theoretical and practical.
We think quantum computing will help us develop the innovations of tomorrow, including AI. That’s why we’re committed to building dedicated quantum hardware and software today.
Quantum computing is a new paradigm that will play a big role in accelerating tasks for AI. We want to offer researchers and developers access to open source frameworks and computing power that can operate beyond classical capabilities.
Focus areas
QuantumCasts
What is a quantum computer?
What is a quantum computer?
In this video, Marissa Giustina addresses some basic questions about quantum computing. You’ll learn about what makes a quantum computer "quantum”, and what differentiates it from a regular computer. In addition, you’ll get to see what Google’s current quantum processors look like, and see the stack of hardware infrastructure needed to run the full system.
Building a quantum computer with superconducting qubits
Building a quantum computer with superconducting qubits
In this episode of QuantumCasts, Daniel Sank discusses the difference between classical and quantum information at the physical level, and how quantum information is harnessed in superconducting devices. You’ll learn what makes a superconducting qubit a quantum mechanical device, as well as some of the challenges researchers face in preserving quantum information.
Programming a quantum computer with Cirq
Programming a quantum computer with Cirq
Want to learn how to program a quantum computer using Cirq? In this episode of QuantumCasts, Dave Bacon (Twitter: @dabacon) teaches you what a quantum program looks like via a simple "hello qubit” program. You’ll also learn about some of the exciting challenges facing quantum programmers today, such as whether Noisy Intermediate-Scale Quantum (NISQ) processors have the ability to solve important practical problems. We’ll also delve a little into how the open source Python framework Cirq was designed to help answer that question.
Quantum supremacy explained
Quantum supremacy explained
Sergio Boixo from the Google AI Quantum applications and algorithms team explains an important milestone for quantum computing known as quantum supremacy. The Google AI Quantum team is currently attempting to achieve this milestone with its own hardware.
Featured publications
Characterizing Quantum Supremacy in Near-Term Devices
Nature Physics, vol. 14 (2018), 595–600
Decoding Quantum Errors Using Subspace Expansions
Nature Communications, vol. 11 (2020), pp. 636
A 28nm Bulk-CMOS 4-to-8GHz <2mW Cryogenic Pulse Modulator for Scalable Quantum Computing
Proceedings of the 2019 International Solid State Circuits Conference, IEEE, pp. 456-458
Fluctuations of Energy-Relaxation Times in Superconducting Qubits
Physical Review Letters, vol. 121 (2018), pp. 090502
Tools
Our open-source frameworks are specifically designed for developing novel quantum algorithms to help solve near-term applications for practical problems.
An open-source quantum framework for building and experimenting with noisy intermediate scale quantum (NISQ) algorithms on near-term quantum processors
An open-source quantum framework for building and experimenting with noisy intermediate scale quantum (NISQ) algorithms on near-term quantum processors
An open-source platform for translating problems in chemistry and materials science into quantum circuits that can be executed on existing platforms
An open-source platform for translating problems in chemistry and materials science into quantum circuits that can be executed on existing platforms
Near-term applications
Quantum Simulation
The design of new materials and elucidation of complex physics through accurate simulations of chemistry and condensed matter models are among the most promising applications of quantum computing.
Error mitigation techniques
We work to develop methods on the road to full quantum error correction that have the capability of dramatically reducing noise in current devices. While full-scale fault tolerant quantum computing may require considerable developments, we have developed the quantum subspace expansion technique to help utilize techniques from quantum error correction to improve performance of applications on near-term devices. Moreover, these techniques facilitate testing of complex quantum codes on near-term devices. We are actively pushing these techniques into new areas and leveraging them as a basis for design of near term experiments.
Quantum Machine Learning
We are developing hybrid quantum-classical machine learning techniques on near-term quantum devices. We are studying universal quantum circuit learning for classification and clustering of quantum and classical data. We are also interested in generative and discriminative quantum neural networks, that could be used as quantum repeaters and state purification units within quantum communication networks, or for verification of other quantum circuits.
Quantum Optimization
Discrete optimizations in aerospace, automotive, and other industries may benefit from hybrid quantum-classical optimization, for example simulated annealing, quantum assisted optimization algorithm (QAOA) and quantum enhanced population transfer may have utility with today’s processors.
Our work
Announcing Cirq: An Open Source Framework for NISQ Algorithms
Announcing Cirq: An Open Source Framework for NISQ Algorithms
Cirq is an open source Python framework for writing, compiling, and running quantum algorithms optimized for today’s quantum computers.
Exploring Quantum Neural Networks
Exploring Quantum Neural Networks
Quantum circuits have the ability to concisely express relationships between input variables that can be intractably difficult for traditional neural networks. Exploring this capability in the context of quantum neural networks for classification problems and how basic properties of quantum systems influence training of these networks is crucial to understanding the role of quantum computers in machine learning.
On the Path to Cryogenic Control of Quantum Processors
On the Path to Cryogenic Control of Quantum Processors
A low-power cryogenic-CMOS single-qubit controller deployed inside the cryostat at 3 Kelvin. This proof of principle prototype motivates future qubit control techniques with lower density of cables in the cryostat.
Understanding Performance Fluctuations in Quantum Processors
Understanding Performance Fluctuations in Quantum Processors
Understanding performance fluctuations in quantum processors lays foundation for improving processor design, fabrication, and calibration.
The Question of Quantum Supremacy
The Question of Quantum Supremacy
Theoretical foundation for our research to demonstrate a computational task that is prohibitively hard for today’s classical computers but which can be carried out experimentally with our quantum processors.
Reformulating Chemistry for More Efficient Quantum Computation
Reformulating Chemistry for More Efficient Quantum Computation
We show how molecules can be represented on quantum computers to simplify the quantum circuits required to solve the problem, and design algorithms for near-term quantum processors with qubits laid out in a linear array.
Some of our people
Quantum Artificial Intelligence will enhance the most consequential of human activities, explaining observations of the world around us.
Hartmut Neven
Engineering Director
Join us