Google AI Quantum

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

Google AI Quantum
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  • Superconducting qubits
    Superconducting qubits have seized an early lead in commercial quantum computing activity. Being first to demonstrate technical quantum supremacy means they are well placed to search for NISQ quantum advantage. However there are significant challenges to be faced in scaling up these systems in the extreme cryogenic environments they require.... Read more
  • Control and measurement plane
    As one of the first quantum platforms to start to significantly scale-up, superconducting qubit platforms have been first to face a series of control plane challenges. These are complicated by the need to work down to extreme cryogenic temperatures. Pioneers face challenges across cryogenics, control electronics and control software. Cryogenics... Read more
  • Qubit Dashboard
    For the references supporting this data please see the accompanying SWOT analysis documents. Fact Based Insight gives precedence to: articles published in refereed journals, arXiv articles, conference presentations, personal correspondence. This data represents Fact Based Insights understanding of the data at the time of publication. We apologise for any misunderstandings... Read more
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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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