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Research Topic : Affective computing
Field of Research : Quantum Physics
Australian State/Territory : ACT
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  • Funded Activity

    Discovery Projects - Grant ID: DP0666262

    Funder
    Australian Research Council
    Funding Amount
    $540,000.00
    Summary
    Solid state optical quantum information technology. There is a significant effort in Australia and around the world to develop quantum information technologies. This project, by demonstrating a suite of critical building blocks for quantum information processing, will support Australia's strong position in this emerging technology. A major motivation for the effort directed at quantum information technologies is the impact they will have on the security of data transmission, both in breakin .... Solid state optical quantum information technology. There is a significant effort in Australia and around the world to develop quantum information technologies. This project, by demonstrating a suite of critical building blocks for quantum information processing, will support Australia's strong position in this emerging technology. A major motivation for the effort directed at quantum information technologies is the impact they will have on the security of data transmission, both in breaking existing encryptions and implementing new encryption systems. The successful completion of this project will provide the essential components for a secure long distance quantum communication network.
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    Active Funded Activity

    Discovery Projects - Grant ID: DP200103760

    Funder
    Australian Research Council
    Funding Amount
    $405,000.00
    Summary
    Quantum-Inspired Machine Learning. This project aims to develop new machine learning techniques based around the close correspondence between neural networks used in deep learning, and tensor networks used in quantum physics. Tensor networks are a form of information compression that is useful in machine learning to construct a compact representation of a large data set in a way that is more amenable to understanding the internal structure than a deep neural network. Expected outcomes of this pr .... Quantum-Inspired Machine Learning. This project aims to develop new machine learning techniques based around the close correspondence between neural networks used in deep learning, and tensor networks used in quantum physics. Tensor networks are a form of information compression that is useful in machine learning to construct a compact representation of a large data set in a way that is more amenable to understanding the internal structure than a deep neural network. Expected outcomes of this project include more resilient algorithms for machine learning, and new ways to represent quantum states that will impact fundamental physics. The resulting benefits include enhanced capacity for cross-discipline collaboration, and improved methods for future industrial applications.
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    Funded Activity

    ARC Future Fellowships - Grant ID: FT110100919

    Funder
    Australian Research Council
    Funding Amount
    $702,684.00
    Summary
    Solid-state quantum communication technology. This project will develop the quantum information devices required to create a quantum communication network for the ultra-secure transmission of data. The key technological challenge is to entangle the quantum state of two crystals separated by kilometres, and maintain this entanglement for many seconds.
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    Showing 1-3 of 3 Funded Activites

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