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In this model there is a hopping termt, a many body interaction term U and an onsite chemical potential term µ

Fermi-Hubbard Model Hubbard Hamiltonians have a simple form, as follows: H =−t ∑ ⟨i,j⟩ ∑ σ={↑,↓} (a† i,σaj,σ +a† j,σai,σ) (3) +U ∑ i a† i,↑ai,↑a† i,↓ai,↓−µ ∑ i ∑ σ={↑,↓} a† i,σai,σ

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Optimizing quantum heuristics with meta-learning

quant-ph · 2019-08-08 · conditional · novelty 5.0

A gradient-based LSTM meta-learner outperforms L-BFGS-B, Bayesian optimization, evolutionary strategies, and Nelder-Mead at tuning QAOA and VQE parameters in simulated noisy settings.

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  • Optimizing quantum heuristics with meta-learning quant-ph · 2019-08-08 · conditional · none · ref 5

    A gradient-based LSTM meta-learner outperforms L-BFGS-B, Bayesian optimization, evolutionary strategies, and Nelder-Mead at tuning QAOA and VQE parameters in simulated noisy settings.