Google Quantum AI

Building quantum processors and algorithms to dramatically accelerate computational tasks for machine intelligence

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Google AI Quantum
Recent Insight
  • Quantum Software Outlook 2022
    IBM continues to lead the quantum cloud, but faces real competition to service early quantum applications. Building the future of this market still looks like a long game. It may be more important to pick a partner with the right strategy than just to compare today’s product features. The post... Read more
  • Quantum Algorithms Outlook 2022
    In contrast to the hype, the early applications of quantum computers look modest. But they do exist. The talented teams developing quantum algorithms still have a fight on their hands to bring forward the date of true broad quantum advantage. What might be achieved and when? The post Quantum Algorithms... Read more
  • Quantum Internet Outlook 2022
    The world needs better cyber security, more so now than ever because of the current threat posed by future quantum computers. A wealth of new techniques are emerging to meet the diverse needs of users. However, to fully appreciate the emerging competitive dynamics and government actions in this sector, we... Read more
Latest News
  • Hybrid Quantum Algorithms for Quantum Monte Carlo
    Posted by William J. Huggins, Research Scientist, Google Quantum AI The intersection between the computational difficulty and practical importance of quantum chemistry challenges run on quantum computers has long been a focus for Google Quantum AI. We’ve experimentally simulated simple... Read more
    Source: Google AI Blog Published on: Mar 16, 2022
  • Resolving High-Energy Impacts on Quantum Processors
    Posted by Matt McEwen, Student Researcher, Google Quantum AI and Lara Faoro, Research Fellow, LPTHE- Sorbonne Université and CNRS (Paris) Quantum processors are made of superconducting quantum bits (qubits) that — being quantum objects — are highly susceptible to even... Read more
    Source: Google AI Blog Published on: Jan 26, 2022
  • Google Research: Themes from 2021 and Beyond
    Posted by Jeff Dean, Senior Fellow and SVP of Google Research, on behalf of the entire Google Research communityOver the last several decades, I've witnessed a lot of change in the fields of machine learning (ML) and computer science. Early... Read more
    Source: Google AI Blog Published on: Jan 11, 2022
  • Demonstrating the Fundamentals of Quantum Error Correction
    Posted by Jimmy Chen, Quantum Research Scientist and Matt McEwen, Student Researcher, Google Quantum AI The Google Quantum AI team has been building quantum processors made of superconducting quantum bits (qubits) that have achieved the first beyond-classical computation, as well... Read more
    Source: Google AI Blog Published on: Aug 11, 2021
  • Achieving Precision in Quantum Material Simulations
    Posted by Charles Neill and Zhang Jiang, Senior Research Scientists, Google Quantum AI In fall of 2019, we demonstrated that the Sycamore quantum processor could outperform the most powerful classical computers when applied to a tailor-made problem. The next challenge... Read more
    Source: Google AI Blog Published on: Jun 25, 2021
Project Description

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.

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