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.
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.
Nature Physics, vol. 14 (2018), 595–600
Nature Communications, vol. 7 (2016)
Our open-source frameworks are specifically designed for developing novel quantum algorithms to help solve near-term applications for practical problems.
Our open source frameworks are specifically designed for developing novel quantum algorithms to help solve near-term applications for practical problems.
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.
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.
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.
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.
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.
It is exhilarating to work on a team which is maniacal about both building a quantum computer and applying the computer to solve problems of great import.