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SUMMARY:Lana Mineh (QET Labs\, University of Bristol)
DTSTART:20200623T070000Z
DTEND:20200623T080000Z
DTSTAMP:20260423T041810Z
UID:UTSQSI/11
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/UTSQSI/11/">
 Strategies for solving the Fermi-Hubbard model on near-term quantum comput
 ers</a>\nby Lana Mineh (QET Labs\, University of Bristol) as part of Centr
 e for Quantum Software and Information Seminar Series\n\n\nAbstract\nThe F
 ermi-Hubbard model is of fundamental importance in condensed-matter physic
 s\, yet is extremely challenging to solve numerically. Finding the ground 
 state of the Hubbard model using variational methods has been predicted to
  be one of the first applications of near-term quantum computers. Here we 
 carry out a detailed analysis and optimisation of the complexity of variat
 ional quantum algorithms for finding the ground state of the Hubbard model
 \, including costs associated with mapping to a real-world hardware platfo
 rm. The depth complexities we find are substantially lower than previous w
 ork. We performed extensive numerical experiments for systems with up to 1
 2 sites. The results suggest that the variational ansätze we used -- an e
 fficient variant of the Hamiltonian Variational ansatz and a novel general
 isation thereof -- will be able to find the ground state of the Hubbard mo
 del with high fidelity in relatively low quantum circuit depth. Our experi
 ments include the effect of realistic measurements and depolarising noise.
  If our numerical results on small lattice sizes are representative of the
  somewhat larger lattices accessible to near-term quantum hardware\, they 
 suggest that optimising over quantum circuits with a gate depth less than 
 a thousand could be sufficient to solve instances of the Hubbard model bey
 ond the capacity of classical exact diagonalisation.\n\nHosted by Michael 
 Bremner\, UTS Centre for Quantum Software and Information.  Email cqsiadmi
 n@uts.edu.au to request interactive zoom link.\n
LOCATION:https://researchseminars.org/talk/UTSQSI/11/
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