Google Quantum AI Lab

We are building quantum processors and algorithms to dramatically accelerate computational tasks for machine intelligence.

About our work

The goal of the Quantum AI team at Google is to build a universal quantum computer. We are developing quantum algorithms in particular with a 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.

The focus of our hardware team is to improve the quality and quantity of the quantum bits (qubits) in our quantum processors. Performing calculations faster than conventional supercomputers requires high fidelity in qubit initialization, operation, and measurement with sufficiently high degree of control and connectivity. We achieve this by researching novel chip architectures and materials.

Our theory group is developing practical algorithms for pre and post-error corrected quantum processors. Examples are quantum chemistry simulations, quantum-assisted optimization, and quantum neural networks. Our cloud team is working to provide access to quantum processors via the Google Cloud Platform. The Quantum AI lab collaborates with universities, national labs, and companies around the world.

Research Areas

Superconducting Qubit Processors
Chip-based scalable architecture,with two-qubit gate fidelities > 99% controlled by microwave electronics.
Qubit Metrology
Reducing two-qubit loss below 0.2% is critical for error correction. We are working on a quantum supremacy experiment, to approximately sample a quantum circuit beyond the capabilities of state-of-the-art classical computers and algorithms.
Quantum Simulation of Chemistry and Materials
Simulation of physical systems is among the most anticipated applications of quantum computing. We especially focus on quantum algorithms for modelling systems of interacting electrons with applications in chemistry and materials science.
Quantum Assisted Optimization
We are developing hybrid quantum-classical solvers for approximate optimization. Thermal jumps in classical algorithms to overcome energy barriers could be enhanced by invoking quantum updates. We are in particular interested in coherent population transfer.
Quantum Neural Networks
We are developing a framework to implement a quantum neural network on near-term processors. We are interested in understanding what advantages may arise from generate massive superposition states during operation of the network.

Tools and Resources


Open source software project for translating problems in chemistry and materials science into representations suitable for quantum processors.

Quantum Chemistry

Video explaining how quantum chemistry may be one of the first applications for quantum processors.

We believe strongly in sharing our findings with the world, fostering an open culture that encourages researchers to disseminate their work among the broader science community.

Some of Our Team

"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."
"Quantum Artificial Intelligence will enhance the most consequential of human activities, explaining observations of the world around us."

Join the Team