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
  • Navigating the quantum storm
    The quantum sector faces a perfect storm of depressed tech markets, lengthening timelines to revenue and unwinding hype. The sector’s long-term potential remains unbound, but startups, investors and governments face a period of challenge. With challenge comes opportunity. The post Navigating the quantum storm appeared first on Fact Based Insight.... Read more
  • 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
Latest News
  • Making a Traversable Wormhole with a Quantum Computer
    Posted by Alexander Zlokapa, Student Researcher, and Hartmut Neven, VP of Engineering, Quantum AI Team Wormholes — wrinkles in the fabric of spacetime that connect two disparate locations — may seem like the stuff of science fiction. But whether or... Read more
    Source: Google AI Blog Published on: Nov 30, 2022
  • Quantum Advantage in Learning from Experiments
    Posted by Jarrod McClean, Staff Research Scientist, Google Quantum AI, and Hsin-Yuan Huang, Graduate Student, Caltech In efforts to learn about the quantum world, scientists face a big obstacle: their classical experience of the world. Whenever a quantum system is... Read more
    Source: Google AI Blog Published on: Jun 22, 2022
  • 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
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.

Project Details
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