quimb.experimental.tnvmc¶

Tools for generic VMC optimization of tensor networks.

Attributes¶

_partial_compose_cache

Classes¶

`NoContext`	A convenience context manager that does nothing.
`MovingStatistics`	Keep track of the windowed mean and estimated variance of a stream of
`DenseSampler`	Sampler that explicitly constructs the full probability distribution.
`DirectTNSampler`
`ClusterSampler`
`ExchangeSampler`
`HamiltonianSampler`
`MetropolisHastingsSampler`
`ComposePartial`
`AmplitudeFactory`
`GradientAccumulator`
`SGD`
`SignDescent`
`RandomSign`
`Adam`
`StochasticReconfigureGradients`
`SR`
`SRADAM`
`TNVMC`

Functions¶

`sample_bitstring_from_prob_ndarray`(p, rng)
`shuffled`(it)	Return a copy of `it` in random order.
`compute_amplitude`(tn, config, chi, optimize)
`compute_amplitudes`(tn, configs, chi, optimize)
`compute_local_energy`(ham, tn, config, chi, optimize)
`draw_config`(edges, config)
`auto_share_multicall`(func, arrays, configs)	Call the function `func`, which should be an array
`get_compose_partial`(f, f_args, f_kwargs, g)
`fuse_unary_ops_`(Z)

Module Contents¶

quimb.experimental.tnvmc.sample_bitstring_from_prob_ndarray(p, rng)¶

quimb.experimental.tnvmc.shuffled(it)¶: Return a copy of it in random order.

class quimb.experimental.tnvmc.NoContext¶

A convenience context manager that does nothing.

__enter__()¶

__exit__(*_, **__)¶

class quimb.experimental.tnvmc.MovingStatistics(window_size)¶

Keep track of the windowed mean and estimated variance of a stream of values on the fly.

window_size¶

xs = []¶

vs = []¶

_xsum = 0.0¶

_vsum = 0.0¶

update(x)¶

property mean¶

property var¶

property std¶

property err¶

class quimb.experimental.tnvmc.DenseSampler(psi=None, seed=None, **contract_opts)¶

Sampler that explicitly constructs the full probability distribution. Useful for debugging small problems.

contract_opts¶

rng¶

_set_psi(psi)¶

sample()¶

update(**kwargs)¶

class quimb.experimental.tnvmc.DirectTNSampler(tn, sweeps=1, max_group_size=8, chi=None, optimize=None, optimize_share_path=False, seed=None, track=False)¶

Parameters:

tn (TensorNetwork) – The tensor network to sample from.
sweeps (int, optional) – The number of sweeps to perform.
max_group_size (int, optional) – The maximum number of sites to include in a single marginal.
chi (int, optional) – The maximum bond dimension to use for compressed contraction.
optimize (PathOptimizer, optional) – The path optimizer to use.
optimize_share_path (bool, optional) – If True, a single path will be used for all contractions regardless of which marginal (i.e. which indices are open) is begin computed.

tn¶

ind2site¶

tid2ind¶

chi¶

sweeps¶

max_group_size¶

optimize¶

optimize_share_path¶

groups = None¶

tree = None¶

path = None¶

rng¶

track¶

plot()¶

calc_groups(**kwargs)¶: Calculate how to group the sites into marginals.

get_groups()¶

calc_path()¶

get_path()¶

get_optimize()¶

contract(tn, output_inds)¶

sample()¶

quimb.experimental.tnvmc.compute_amplitude(tn, config, chi, optimize)¶

quimb.experimental.tnvmc.compute_amplitudes(tn, configs, chi, optimize)¶

quimb.experimental.tnvmc.compute_local_energy(ham, tn, config, chi, optimize)¶

quimb.experimental.tnvmc.draw_config(edges, config)¶

class quimb.experimental.tnvmc.ClusterSampler(psi=None, max_distance=1, use_gauges=True, seed=None, contract_opts=None)¶

rng¶

use_gauges¶

max_distance¶

contract_opts¶

_set_psi(psi)¶

sample()¶

candidate¶

accept(config)¶

update(**kwargs)¶

class quimb.experimental.tnvmc.ExchangeSampler(edges, seed=None)¶

edges¶

Ne¶

sites¶

N¶

rng¶

values0¶

config¶

candidate()¶

accept(config)¶

sample()¶

update(**_)¶

class quimb.experimental.tnvmc.HamiltonianSampler(ham, seed=None)¶

ham¶

rng¶

N¶

values0¶

config¶

candidate()¶

accept(config)¶

sample()¶

update(**_)¶

class quimb.experimental.tnvmc.MetropolisHastingsSampler(sub_sampler, amplitude_factory=None, initial=None, burn_in=0, seed=None, track=False)¶

sub_sampler¶

seed¶

rng¶

accepted = 0¶

total = 0¶

burn_in¶

track¶

property acceptance_ratio¶

sample()¶

update(**kwargs)¶

plot()¶

quimb.experimental.tnvmc.auto_share_multicall(func, arrays, configs)¶: Call the function func, which should be an array function making use of autoray dispatched calls, multiple times, automatically reusing shared intermediates.

class quimb.experimental.tnvmc.ComposePartial(f, f_args, f_kwargs, g)¶

__slots__ = ('f', 'f_args', 'f_kwargs', 'g')¶

f¶

f_args¶

f_kwargs¶

g¶

__call__(*args, **kwargs)¶

quimb.experimental.tnvmc._partial_compose_cache¶

quimb.experimental.tnvmc.get_compose_partial(f, f_args, f_kwargs, g)¶

quimb.experimental.tnvmc.fuse_unary_ops_(Z)¶

class quimb.experimental.tnvmc.AmplitudeFactory(psi=None, contract_fn=None, maxsize=2**20, autojit_opts=(), **contract_opts)¶

contract_fn¶

contract_opts¶

autojit_opts¶

store¶

hits = 0¶

queries = 0¶

_set_psi(psi)¶

compute_single(config)¶: Compute the amplitude of config, making use of autojit.

compute_multi(configs)¶: Compute the amplitudes corresponding to the sequence configs, making use of shared intermediates.

amplitude(config)¶: Get the amplitude of config, either from the cache or by computing it.

amplitudes(configs)¶

prob(config)¶: Calculate the probability of a configuration.

clear()¶

__contains__(config)¶

__setitem__(config, c)¶

__getitem__(config)¶

__repr__()¶

class quimb.experimental.tnvmc.GradientAccumulator¶

_grads_logpsi = None¶

_grads_energy = None¶

_batch_energy = None¶

_num_samples = 0¶

_init_storage(grads)¶

update(grads_logpsi_sample, local_energy)¶

extract_grads_energy()¶

class quimb.experimental.tnvmc.SGD(learning_rate=0.01)¶

Bases: GradientAccumulator

learning_rate¶

transform_gradients()¶

class quimb.experimental.tnvmc.SignDescent(learning_rate=0.01)¶

Bases: GradientAccumulator

learning_rate¶

transform_gradients()¶

class quimb.experimental.tnvmc.RandomSign(learning_rate=0.01)¶

Bases: GradientAccumulator

learning_rate¶

transform_gradients()¶

class quimb.experimental.tnvmc.Adam(learning_rate=0.01, beta1=0.9, beta2=0.999, eps=1e-08)¶

Bases: GradientAccumulator

learning_rate¶

beta1¶

beta2¶

eps¶

_num_its = 0¶

_ms = None¶

_vs = None¶

transform_gradients()¶

class quimb.experimental.tnvmc.StochasticReconfigureGradients(delta=1e-05)¶

delta¶

vectorizer = None¶

gs = []¶

update(grads_logpsi_sample, local_energy)¶

extract_grads_energy()¶

class quimb.experimental.tnvmc.SR(learning_rate=0.05, delta=1e-05)¶: Bases: SGD, StochasticReconfigureGradients

class quimb.experimental.tnvmc.SRADAM(learning_rate=0.01, beta1=0.9, beta2=0.999, eps=1e-08, delta=1e-05)¶: Bases: Adam, StochasticReconfigureGradients

class quimb.experimental.tnvmc.TNVMC(psi, ham, sampler, conditioner='auto', learning_rate=0.01, optimizer='adam', optimizer_opts=None, track_window_size=1000, **contract_opts)¶

psi¶

ham¶

sampler¶

optimizer_opts¶

optimizer¶

contract_opts¶

amplitude_factory¶

moving_stats¶

local_energies¶

energies¶

energy_errors¶

num_tensors¶

nsites¶

_progbar = None¶

_compute_log_gradients_torch(config)¶

_compute_local_energy(config)¶

_run(steps, batchsize)¶

run(total=10000, batchsize=100, progbar=True)¶

measure(max_samples=10000, rtol=0.0001, progbar=True)¶

plot(figsize=(12, 6), yrange_quantile=(0.01, 0.99), zoom='auto', hlines=())¶