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Showing 1–15 of 15 results
Advanced filters: Author: Giuseppe Carleo Clear advanced filters

  • A numerical approach capable of simulating large-scale Rydberg atom quantum systems suggests that protocols for preparing topological states can produce experimental signatures of these states without reaching a topological phase.

    • Linda Mauron
    • Zakari Denis
    • Giuseppe Carleo
    ResearchOpen Access
    Nature Physics
    Volume: 21, P: 1332-1337

  • Neural-Network Quantum States are highly effective in describing quantum many-body systems, but they are typically system-specific. Here the authors propose Foundation Neural-Network Quantum States, which can be applied across multiple systems simultaneously, enabling accurate estimation of challenging observables.

    • Riccardo Rende
    • Luciano Loris Viteritti
    • Giuseppe Carleo
    ResearchOpen Access
    Nature Communications
    Volume: 16, P: 1-12

  • Variational parameterization of many-body wavefunctions using neural network quantum states is a powerful technique for studying many-body quantum systems but has been limited to time-independent cases. Nys et al. extend this approach to real-time evolution, providing improved accuracy over traditional methods.

    • Jannes Nys
    • Gabriel Pescia
    • Giuseppe Carleo
    ResearchOpen Access
    Nature Communications
    Volume: 15, P: 1-11

  • Quantum Monte Carlo methods using neutral-network ansatzes can provide virtually exact solutions to the electronic Schrödinger equations for small systems and are comparable to conventional quantum chemistry methods when investigating systems with dozens of electrons.

    • Jan Hermann
    • James Spencer
    • Frank Noé
    Reviews
    Nature Reviews Chemistry
    Volume: 7, P: 692-709

  • 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

  • Despite the importance of neural-network quantum states, representing fermionic matter is yet to be fully achieved. Here the authors map fermionic degrees of freedom to spin ones and use neural-networks to perform electronic structure calculations on model diatomic molecules to achieve chemical accuracy.

    • Kenny Choo
    • Antonio Mezzacapo
    • Giuseppe Carleo
    ResearchOpen Access
    Nature Communications
    Volume: 11, P: 1-7

  • The signatures of water, carbon monoxide, hydrogen cyanide, methane, ammonia and acetylene are observed in the transmission spectrum of the hot Jupiter HD 209458b, with abundance ratios suggesting a super-solar carbon-to-oxygen ratio.

    • Paolo Giacobbe
    • Matteo Brogi
    • Andrea Tozzi
    Research
    Nature
    Volume: 592, P: 205-208

  • Significant improvements in numerical methods for quantum systems often come from finding new ways of representing quantum states that can be optimized and simulated more efficiently. Here the authors demonstrate a method to calculate exact neural network representations of many-body ground states.

    • Giuseppe Carleo
    • Yusuke Nomura
    • Masatoshi Imada
    ResearchOpen Access
    Nature Communications
    Volume: 9, P: 1-11

  • 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

  • In this manuscript, the authors introduce a method for numerically modelling fermionic systems using a neural network wavefunction ansatz. They demonstrate that this method can efficiently and accurately find ground and low-lying excited states in two-dimensional models, outperforming existing approaches.

    • Imelda Romero
    • Jannes Nys
    • Giuseppe Carleo
    ResearchOpen Access
    Communications Physics
    Volume: 8, P: 1-10

  • A type of stochastic neural network called a restricted Boltzmann machine has been widely used in artificial intelligence applications for decades. They are now finding new life in the simulation of complex wavefunctions in quantum many-body physics.

    • Roger G. Melko
    • Giuseppe Carleo
    • J. Ignacio Cirac
    Reviews
    Nature Physics
    Volume: 15, P: 887-892

  • The theoretical description of ultra-cold Fermi gases is challenging due to the presence of strong, short-ranged interactions. This work introduces a Pfaffian-Jastrow neural-network quantum state that outperforms existing Slater-Jastrow frameworks and diffusion Monte Carlo methods.

    • Jane Kim
    • Gabriel Pescia
    • Alessandro Lovato
    ResearchOpen Access
    Communications Physics
    Volume: 7, P: 1-12