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Showing 1–6 of 6 results
Advanced filters: Author: Antonio Mezzacapo Clear advanced filters
  • The estimation of low energies of many-body systems is a cornerstone of the computational quantum sciences. This paper demonstrates on a superconducting quantum processor that the Krylov quantum diagonalization algorithm is poised to complement its classical counterparts at the foundation of computational methods for quantum systems.

    • Nobuyuki Yoshioka
    • Mirko Amico
    • Antonio Mezzacapo
    ResearchOpen Access
    Nature Communications
    Volume: 16, P: 1-8
  • 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 authors present sample-based quantum diagonalization (SQD) simulations of non-covalent interactions that match the accuracy of state-of-the-art classical methods. These results mark a key step towards quantum advantage, though further advances are needed to fully realize this potential.

    • Danil Kaliakin
    • Akhil Shajan
    • Kenneth M. Merz Jr
    ResearchOpen Access
    Communications Physics
    Volume: 8, P: 1-12
  • 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