Transformer neural networks and quantum simulators: a hybrid approach for simulating strongly correlated systems

Understand

Owing to their great expressivity and versatility, neural networks have gained attention for simulating large two-dimensional quantum many-body systems.

• However, their expressivity comes with the cost of a challenging optimization due to the in general rugged and complicated loss landscape.
• Here, we present a hybrid optimization scheme for neural quantum states (NQS), involving a data-driven pretraining with numerical or experimental data and a second, Hamiltonian-driven optimization stage.
• By using both projective measurements from the computational basis as well as expectation values from other measurement configurations such as spin-spin correlations, our pretraining gives access to the sign structure of the state, yielding improved and faster convergence that is robust w.r.t.