Fermi-Hubbard models
Emerging18papers using it
2024first seen
The 'Fermi-Hubbard models' dataset contains data on quantum many-body ground states used to evaluate the effectiveness of an unsupervised machine-learning framework in discovering compressed representations and optimizing energy minimization within latent spaces.
Papers using Fermi-Hubbard models (6)
- Imaginary-time-enhanced feedback-based quantum algorithms for universal ground-state preparationFiltered Quantum Phase EstimationPhase-Sensitive Measurements on a Fermi-Hubbard Quantum ProcessorFaster Quantum Algorithm for Multiple Observables Estimation in
Fermionic ProblemsUnlocking early fault-tolerant quantum computing with mitigated magic dilutionShot-noise reduction for lattice Hamiltonians