quantax.optimizer.SPRING

class quantax.optimizer.SPRING(state: Variational, hamiltonian: Operator, imag_time: bool = True, solver: Callable | None = None, mu: float = 0.9, file: None | str | Path | BinaryIO = None)

SPRING optimizer. This is a variant of SR with momentum. When using the default value of mu=0.9, the learning rate should be roughly 1/5 of the one in SR.

__init__(state: Variational, hamiltonian: Operator, imag_time: bool = True, solver: Callable | None = None, mu: float = 0.9, file: None | str | Path | BinaryIO = None)

Parameters:

• state – Variational state to be optimized.

• hamiltonian – The Hamiltonian for the evolution.

• imag_time – Whether to use imaginary-time evolution, default to True.

• solver – The numerical solver for the matrix inverse, default to auto_pinv_eig.

Methods

__init__(state, hamiltonian[, imag_time, ...])
get_Ebar(samples)	Compute \(\bar \epsilon\) for given samples.
get_Obar(samples)	Calculate \(\bar O = \frac{1}{\sqrt{N_s}}(\frac{1}{\psi} \frac{\partial \psi}{\partial \theta} - \left< \frac{1}{\psi} \frac{\partial \psi}{\partial \theta} \right>)\) for given samples.
get_step(samples)	Obtain the optimization step by solving the equation \(\bar O \dot \theta = \bar \epsilon\) for given samples.
save(file)	Save the optimizer internal quantities to a file.
solve(Obar, Ebar)	Solve the equation \(\bar O \dot \theta = \bar \epsilon\) for given \(\bar O\) and \(\bar \epsilon\).

Attributes

VarE	Energy variance \(\left< (H - E)^2 \right>\) of the current step.
energy	Energy of the current step.
hamiltonian	The Hamiltonian for the evolution.
holomorphic	Whether the state is holomorphic.
imag_time	Whether to use imaginary-time evolution.
state	Variational state to be optimized.
vs_type	The vs_type of the state.