quimb.experimental.belief_propagation.l2bp

Module Contents

Classes

L2BP

Lazy (as in multiple uncontracted tensors per site) 2-norm (as in for

Functions

contract_l2bp(tn[, site_tags, damping, update, ...])

Estimate the norm squared of tn using lazy belief propagation.

compress_l2bp(tn, max_bond[, cutoff, cutoff_mode, ...])

Compress tn using lazy belief propagation, producing a tensor
class quimb.experimental.belief_propagation.l2bp.L2BP(tn, site_tags=None, damping=0.0, update='sequential', local_convergence=True, optimize='auto-hq', **contract_opts)

Bases: quimb.experimental.belief_propagation.bp_common.BeliefPropagationCommon

Lazy (as in multiple uncontracted tensors per site) 2-norm (as in for wavefunctions and operators) belief propagation.

Parameters:

  • tn (TensorNetwork) – The tensor network to form the 2-norm of and run BP on.

  • site_tags (sequence of str, optional) – The tags identifying the sites in tn, each tag forms a region, which should not overlap. If the tensor network is structured, then these are inferred automatically.

  • damping (float, optional) – The damping parameter to use, defaults to no damping.

  • update ({'parallel', 'sequential'}, optional) – Whether to update all messages in parallel or sequentially.

  • local_convergence (bool, optional) – Whether to allow messages to locally converge - i.e. if all their input messages have converged then stop updating them.

  • optimize (str or PathOptimizer, optional) – The path optimizer to use when contracting the messages.

  • contract_opts – Other options supplied to cotengra.array_contract.

iterate(tol=5e-06)
normalize_messages()

Normalize all messages such that for each bond <m_i|m_j> = 1 and <m_i|m_i> = <m_j|m_j> (but in general != 1).

contract(strip_exponent=False)

Estimate the contraction of the norm squared using the current messages.

partial_trace(site, normalized=True, optimize='auto-hq')
compress(tn, max_bond=None, cutoff=5e-06, cutoff_mode='rsum2', renorm=0, lazy=False)

Compress the state tn, assumed to matched this L2BP instance, using the messages stored.

quimb.experimental.belief_propagation.l2bp.contract_l2bp(tn, site_tags=None, damping=0.0, update='sequential', local_convergence=True, optimize='auto-hq', max_iterations=1000, tol=5e-06, strip_exponent=False, info=None, progbar=False, **contract_opts)

Estimate the norm squared of tn using lazy belief propagation.

Parameters:

  • tn (TensorNetwork) – The tensor network to estimate the norm squared of.

  • site_tags (sequence of str, optional) – The tags identifying the sites in tn, each tag forms a region.

  • damping (float, optional) – The damping parameter to use, defaults to no damping.

  • update ({'parallel', 'sequential'}, optional) – Whether to update all messages in parallel or sequentially.

  • local_convergence (bool, optional) – Whether to allow messages to locally converge - i.e. if all their input messages have converged then stop updating them.

  • optimize (str or PathOptimizer, optional) – The contraction strategy to use.

  • max_iterations (int, optional) – The maximum number of iterations to perform.

  • tol (float, optional) – The convergence tolerance for messages.

  • strip_exponent (bool, optional) – Whether to strip the exponent from the final result. If True then the returned result is (mantissa, exponent).

  • info (dict, optional) – If specified, update this dictionary with information about the belief propagation run.

  • progbar (bool, optional) – Whether to show a progress bar.

  • contract_opts – Other options supplied to cotengra.array_contract.

quimb.experimental.belief_propagation.l2bp.compress_l2bp(tn, max_bond, cutoff=0.0, cutoff_mode='rsum2', max_iterations=1000, tol=5e-06, site_tags=None, damping=0.0, update='sequential', local_convergence=True, optimize='auto-hq', lazy=False, inplace=False, info=None, progbar=False, **contract_opts)

Compress tn using lazy belief propagation, producing a tensor network with a single tensor per site.

Parameters:

  • tn (TensorNetwork) – The tensor network to form the 2-norm of, run BP on and then compress.

  • max_bond (int) – The maximum bond dimension to compress to.

  • cutoff (float, optional) – The cutoff to use when compressing.

  • cutoff_mode (int, optional) – The cutoff mode to use when compressing.

  • max_iterations (int, optional) – The maximum number of iterations to perform.

  • tol (float, optional) – The convergence tolerance for messages.

  • site_tags (sequence of str, optional) – The tags identifying the sites in tn, each tag forms a region. If the tensor network is structured, then these are inferred automatically.

  • damping (float, optional) – The damping parameter to use, defaults to no damping.

  • update ({'parallel', 'sequential'}, optional) – Whether to update all messages in parallel or sequentially.

  • local_convergence (bool, optional) – Whether to allow messages to locally converge - i.e. if all their input messages have converged then stop updating them.

  • optimize (str or PathOptimizer, optional) – The path optimizer to use when contracting the messages.

  • lazy (bool, optional) – Whether to perform the compression lazily, i.e. to leave the computed compression projectors uncontracted.

  • inplace (bool, optional) – Whether to perform the compression inplace.

  • info (dict, optional) – If specified, update this dictionary with information about the belief propagation run.

  • progbar (bool, optional) – Whether to show a progress bar.

  • contract_opts – Other options supplied to cotengra.array_contract.

Return type:

TensorNetwork