Google AI Quantum

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

Google AI Quantum
Recent Insight
  • Quantum Outlook 2020
    2020 will be a watershed year for the Quantum Revolution. There will be surprising early applications, though previously over-hyped timelines may leave some disappointed. Geopolitical considerations will be hard to ignore as major national programmes set out their stalls. The chill winds of a quantum winter may threaten some, but ... read more
    Source: Fact Based Insight – prepare for tomorrowDecember 16, 2019
  • Quantum Hardware Outlook 2020
    2019 saw Google finally demonstrate quantum supremacy. Competitors such as IBM reminded us that this was just an opening skirmish in what will be a long campaign. In 2020 we will see paths divide as competing companies and technologies face-up to the quantum chasm blocking the way to large scale ... read more
    Source: Fact Based Insight – prepare for tomorrowDecember 16, 2019
  • Quantum Algorithms Outlook 2020
    Contrary to many reports there are set be immediate applications even for early devices. Some will be controversial. However in other areas timelines look over-hyped. 2020 will be a key year to prove the naysayers wrong. Will clients see early value? Despite all the fuss, quantum processors actually run more ... read more
    Source: Fact Based Insight – prepare for tomorrowDecember 16, 2019
Latest News
  • Google Research: Looking Back at 2019, and Forward to 2020 and Beyond
    Posted by Jeff Dean, Senior Fellow and SVP of Google Research and Health, on behalf of the entire Google Research community The goal of Google Research is to work on long-term, ambitious problems, with an emphasis on solving ones that will dramatically help people throughout their daily lives. In pursuit ... read more
    Source: Google AI BlogJanuary 9, 2020
  • New Solutions for Quantum Gravity with TensorFlow
    Posted by Thomas Fischbacher, Researcher in Compression, Google Research, Zürich Recent strides in machine learning (ML) research have led to the development of tools useful for research problems well beyond the realm for which they were designed. The value of these tools when applied to topics ranging from teaching robots ... read more
    Source: Google AI BlogNovember 15, 2019
  • Quantum Supremacy Using a Programmable Superconducting Processor
    Posted by John Martinis, Chief Scientist Quantum Hardware and Sergio Boixo, Chief Scientist Quantum Computing Theory, Google AI Quantum Physicists have been talking about the power of quantum computing for over 30 years, but the questions have always been: will it ever do something useful and is it worth investing ... read more
    Source: Google AI BlogOctober 23, 2019
  • Improving Quantum Computation with Classical Machine Learning
    Posted by Murphy Yuezhen Niu and Sergio Boixo, Research Scientists One of the primary challenges for the realization of near-term quantum computers has to do with their most basic constituent: the qubit. Qubits can interact with anything in close proximity that carries energy close to their own—stray photons (i.e., unwanted electromagnetic ... read more
    Source: Google AI BlogOctober 3, 2019
  • Announcement of the 2019 Fellowship Awardees and Highlights from the Google PhD Fellowship Summit
    Posted by Susie Kim, Program Manager, University Relations In 2009, Google created the PhD Fellowship Program to recognize and support outstanding graduate students who are doing exceptional research in Computer Science and related fields who seek to influence the future of technology. Now in its eleventh year, these Fellowships have ... read more
    Source: Google AI BlogSeptember 5, 2019
  • Introducing TensorNetwork, an Open Source Library for Efficient Tensor Calculations
    Posted by Chase Roberts, Research Engineer, Google AI and Stefan Leichenauer, Research Scientist, X Many of the world's toughest scientific challenges, like developing high-temperature superconductors and understanding the true nature of space and time, involve dealing with the complexity of quantum systems. What makes these challenges difficult is that the ... read more
    Source: Google AI BlogJune 4, 2019
  • Google Faculty Research Awards 2018
    Posted by Maggie Johnson, VP, Education and Negar Saei, Program Manager, University Relations We just completed another round of the Google Faculty Research Awards, our annual open call for proposals on computer science and related topics, such as quantum computing, machine learning, algorithms and theory, natural language processing and more. ... read more
    Source: Google AI BlogMarch 15, 2019
  • On the Path to Cryogenic Control of Quantum Processors
    Posted by Joseph Bardin, Visiting Faculty Researcher and Erik Lucero, Staff Research Scientist and Hardware Lead, Google AI Quantum TeamBuilding a quantum computer that can solve practical problems that would otherwise be classically intractable due to the computation complexity, cost, energy consumption or time to solution, is the longstanding goal ... read more
    Source: Google AI BlogFebruary 21, 2019
  • Looking Back at Google’s Research Efforts in 2018
    Posted by Jeff Dean, Senior Fellow and Google AI Lead, on behalf of the entire Google Research Community2018 was an exciting year for Google's research teams, with our work advancing technology in many ways, including fundamental computer science research results and publications, the application of our research to emerging areas ... read more
    Source: Google AI BlogJanuary 15, 2019
  • Exploring Quantum Neural Networks
    Posted by Jarrod McClean, Senior Research Scientist and Hartmut Neven, Director of Engineering, Google AI Quantum TeamSince its inception, the Google AI Quantum team has pushed to understand the role of quantum computing in machine learning. The existence of algorithms with provable advantages for global optimization suggest that quantum computers ... read more
    Source: Google AI BlogDecember 17, 2018
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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