quantax.sampler.ParticleHop#

class quantax.sampler.ParticleHop#

Bases: Metropolis

Generate Monte Carlo samples by hopping random particles to neighbor sites. The sampler will automatically choose to hop particles or holes, in the spin case spin up or down.

Warning

This sampler only works if all sites have the same amount of neighbors.

__init__(state: State, nsamples: int, reweight: float = 2.0, thermal_steps: int | None = None, sweep_steps: int | None = None, initial_spins: Array | None = None, n_neighbor: int | Sequence[int] = 1)#
Parameters:

  • state – The state used for computing the wave function and probability. Since exchanging neighbor spins doesn’t change the total Sz, the state must have quantax.symmetry.ParticleConserve symmetry to specify the symmetry sector.

  • nsamples – Number of samples generated per iteration. It should be a multiple of the total number of machines to allow samples to be equally distributed on different machines.

  • reweight – The reweight factor n defining the sample probability \(|\psi|^n\), default to 2.0.

  • thermal_steps – The number of thermalization steps in the beginning of each Markov chain, default to be 20 * fock state length.

  • sweep_steps – The number of steps for generating new samples, default to be 2 * fock state length.

  • initial_spins – The initial spins for every Markov chain before the thermalization steps, default to be random spins.

  • n_neighbor – The neighbors to be considered by particle hoppings, default to nearest neighbors.

property nflips: int#

The number of flips in new proposal.

property is_balanced: bool#

Whether the sampler has balanced proposal rate \(P(s'|s) = P(s|s')\), default to True

property nsamples: int#

Number of samples generated per iteration

reset() → None#

Reset all Markov chains to initial_spins and thermalize them

property reweight: float#

The reweight factor n defining the sample probability \(|\psi|^n\)

property state: State#

The state used for computing the wave function and probability

sweep(nsweeps: int | None = None) → Samples#

Generate new samples

Parameters:

nsweeps – Number of sweeps for generating the new samples, default to be self._sweep_steps