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Showing 1–6 of 6 results
Advanced filters: Author: Giacomo Torlai Clear advanced filters

  • In quantum technologies, scalable ways to characterise errors in quantum hardware are highly needed. Here, the authors propose an approximate version of quantum process tomography based on tensor network representations of the processes and data-driven optimisation.

    • Giacomo Torlai
    • Christopher J. Wood
    • Leandro Aolita
    ResearchOpen Access
    Nature Communications
    Volume: 14, P: 1-10

  • Present day quantum technologies enable computations with tens and soon hundreds of qubits. A major outstanding challenge is to measure and benchmark the complete quantum state, a task that grows exponentially with the system size. Generative models based on restricted Boltzmann machines and recurrent neural networks can be employed to solve this quantum tomography problem in a scalable manner.

    • Juan Carrasquilla
    • Giacomo Torlai
    • Leandro Aolita
    Research
    Nature Machine Intelligence
    Volume: 1, P: 155-161

  • Unsupervised machine learning techniques can efficiently perform quantum state tomography of large, highly entangled states with high accuracy, and allow the reconstruction of many-body quantities from simple experimentally accessible measurements.

    • Giacomo Torlai
    • Guglielmo Mazzola
    • Giuseppe Carleo
    Research
    Nature Physics
    Volume: 14, P: 447-450

  • Frustrated geometries in Rydberg atom arrays present challenges for conventional simulations, particularly in exploring exotic many-body states like spin liquids and glasses. The authors employ 2D recurrent neural network wave functions to study ground states on the Kagome lattice, revealing no evidence of exotic phases and highlighting the potential of autoregressive models in overcoming simulation limitations.

    • Mohamed Hibat-Allah
    • Ejaaz Merali
    • Juan Carrasquilla
    ResearchOpen Access
    Communications Physics
    Volume: 8, P: 1-8