quantax.sampler.MixSampler

class quantax.sampler.MixSampler(samplers: Sequence[Metropolis], reweight: float = 2.0, thermal_steps: int | None = None, sweep_steps: int | None = None, initial_spins: Array | None = None)

A mixture of several metropolis samplers. New samples are proposed randomly by every sampler.

Warning

This sampler only works for ingredient samplers with balanced proposal rates \(P(s'|s) = P(s|s')\)

__init__(samplers: Sequence[Metropolis], reweight: float = 2.0, thermal_steps: int | None = None, sweep_steps: int | None = None, initial_spins: Array | None = None)

Parameters:

• state – The state used for computing the wave function and probability.

• 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.

Methods

__init__(samplers[, reweight, ...])
reset()	Reset all Markov chains to initial_spins and thermalize them
sweep([nsweeps])	Generate new samples

Attributes

N
is_balanced	Whether the sampler has balanced proposal rate \(P(s'|s) = P(s|s')\), default to True
nflips	The number of flips in new proposal.
nsamples	Number of samples generated per iteration
nstates
reweight	The reweight factor n defining the sample probability \(|\psi|^n\)
state	The state used for computing the wave function and probability