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Showing 1–8 of 8 results
Advanced filters: Author: Kristan Temme Clear advanced filters

  • The kernel method in machine learning can be implemented on near-term quantum computers. A 27-qubit device has now been used to solve learning problems using kernels that have the potential to be practically useful.

    • Jennifer R. Glick
    • Tanvi P. Gujarati
    • Kristan Temme
    Research
    Nature Physics
    Volume: 20, P: 479-483

  • Two classification algorithms that use the quantum state space to produce feature maps are demonstrated on a superconducting processor, enabling the solution of problems when the feature space is large and the kernel functions are computationally expensive to estimate.

    • Vojtěch Havlíček
    • Antonio D. Córcoles
    • Jay M. Gambetta
    Research
    Nature
    Volume: 567, P: 209-212

  • Experiments on a noisy 127-qubit superconducting quantum processor report the accurate measurement of expectation values beyond the reach of current brute-force classical computation, demonstrating evidence for the utility of quantum computing before fault tolerance.

    • Youngseok Kim
    • Andrew Eddins
    • Abhinav Kandala
    ResearchOpen Access
    Nature
    Volume: 618, P: 500-505

  • Many quantum machine learning algorithms have been proposed, but it is typically unknown whether they would outperform classical methods on practical devices. A specially constructed algorithm shows that a formal quantum advantage is possible.

    • Yunchao Liu
    • Srinivasan Arunachalam
    • Kristan Temme
    Research
    Nature Physics
    Volume: 17, P: 1013-1017