RandomUnitaryEvolution¶
full name: tenpy.algorithms.tebd.RandomUnitaryEvolution
parent module:
tenpy.algorithms.tebd
type: class
-
class
tenpy.algorithms.tebd.
RandomUnitaryEvolution
(psi, TEBD_params)[source]¶ Bases:
tenpy.algorithms.tebd.Engine
Evolution of an MPS with random two-site unitaries in a TEBD-like fashion.
Instead of using a model Hamiltonian, this TEBD engine evolves with random two-site unitaries. These unitaries are drawn according to the Haar measure on unitaries obeying the conservation laws dictated by the conserved charges. If no charge is preserved, this distribution is called circular unitary ensemble (CUE), see
CUE()
.On one hand, such an evolution is of interest in recent research (see eg. arXiv:1710.09827). On the other hand, it also comes in handy to “randomize” an initial state, e.g. for DMRG. Note that the entanglement grows very quickly, choose the truncation paramters accordingly!
- Parameters
Examples
One can initialize a “random” state with total Sz = L//2 as follows:
>>> L = 8 >>> spin_half = SpinHalfSite(conserve='Sz') >>> psi = MPS.from_product_state([spin_half]*L, [0, 1]*(L//2), bc='finite') # Neel state >>> print(psi.chi) [1, 1, 1, 1, 1, 1, 1] >>> TEBD_params = dict(N_steps=2, trunc_params={'chi_max':10}) >>> eng = RandomUnitaryEvolution(psi, TEBD_params) >>> eng.run() >>> print(psi.chi) [2, 4, 8, 10, 8, 4, 2] >>> psi.canonical_form() # necessary if you need to truncate (strongly) during the evolution
The “random” unitaries preserve the specified charges, e.g. here we have Sz-conservation. If you start in a sector of all up spins, the random unitaries can only apply a phase:
>>> psi2 = MPS.from_product_state([spin_half]*L, [0]*L, bc='finite') # all spins up >>> print(psi2.chi) [1, 1, 1, 1, 1, 1, 1] >>> eng2 = RandomUnitaryEvolution(psi2, TEBD_params) >>> eng2.run() # random unitaries respect Sz conservation -> we stay in all-up sector >>> print(psi2.chi) # still a product state, not really random!!! [1, 1, 1, 1, 1, 1, 1]
- Attributes
trunc_err_bonds
truncation error introduced on each non-trivial bond.
Methods
calc_U
(self)Draw new random two-site unitaries replacing the usual U of TEBD.
run
(self)Time evolution with TEBD (time evolving block decimation) and random two-site unitaries.
run_GS
(self)TEBD algorithm in imaginary time to find the ground state.
suzuki_trotter_decomposition
(order, N_steps)Returns list of necessary steps for the suzuki trotter decomposition.
suzuki_trotter_time_steps
(order)Return time steps of U for the Suzuki Trotter decomposition of desired order.
update
(self, N_steps)Apply
N_steps
random two-site unitaries to each bond (in even-odd pattern).update_bond
(self, i, U_bond)Updates the B matrices on a given bond.
update_bond_imag
(self, i, U_bond)Update a bond with a (possibly non-unitary) U_bond.
update_imag
(self, N_steps)Perform an update suitable for imaginary time evolution.
update_step
(self, U_idx_dt, odd)Updates either even or odd bonds in unit cell.
-
run
(self)[source]¶ Time evolution with TEBD (time evolving block decimation) and random two-site unitaries.
The following (optional) parameters are read out from the
TEBD_params
.key
type
description
N_steps
int
Number of two-site unitaries to be applied on each bond.
trunc_params
dict
Truncation parameters as described in
truncate()
-
update
(self, N_steps)[source]¶ Apply
N_steps
random two-site unitaries to each bond (in even-odd pattern).- Parameters
- N_stepsint
The number of steps for which the whole lattice should be updated.
- Returns
- trunc_err
TruncationError
The error of the represented state which is introduced due to the truncation during this sequence of update steps.
- trunc_err
-
run_GS
(self)¶ TEBD algorithm in imaginary time to find the ground state.
Note
It is almost always more efficient (and hence advisable) to use DMRG. This algorithms can nonetheless be used quite well as a benchmark and for comparison.
The following (optional) parameters are read out from the
TEBD_params
. Useverbose=1
to print the used parameters during runtime.key
type
description
delta_tau_list
list
A list of floats: the timesteps to be used. Choosing a large timestep delta_tau introduces large (Trotter) errors, but a too small time step requires a lot of steps to reach
exp(-tau H) --> |psi0><psi0|
. Therefore, we start with fairly large time steps for a quick time evolution until convergence, and the gradually decrease the time step.order
int
Order of the Suzuki-Trotter decomposition.
N_steps
int
Number of steps before measurement can be performed
trunc_params
dict
Truncation parameters as described in
truncate()
-
static
suzuki_trotter_decomposition
(order, N_steps)¶ Returns list of necessary steps for the suzuki trotter decomposition.
We split the Hamiltonian as \(H = H_{even} + H_{odd} = H[0] + H[1]\). The Suzuki-Trotter decomposition is an approximation \(\exp(t H) \approx prod_{(j, k) \in ST} \exp(d[j] t H[k]) + O(t^{order+1 })\).
- Parameters
- orderint
The desired order of the Suzuki-Trotter decomposition.
- Returns
- ST_decompositionlist of (int, int)
Indices
j, k
of the time-stepsd = suzuki_trotter_time_step(order)
and the decomposition of H. They are chosen such that a subsequent application ofexp(d[j] t H[k])
to a given state|psi>
yields(exp(N_steps t H[k]) + O(N_steps t^{order+1}))|psi>
.
-
static
suzuki_trotter_time_steps
(order)¶ Return time steps of U for the Suzuki Trotter decomposition of desired order.
See
suzuki_trotter_decomposition()
for details.- Parameters
- orderint
The desired order of the Suzuki-Trotter decomposition.
- Returns
- time_stepslist of float
We need
U = exp(-i H_{even/odd} delta_t * dt)
for the dt returned in this list.
-
property
trunc_err_bonds
¶ truncation error introduced on each non-trivial bond.
-
update_bond
(self, i, U_bond)¶ Updates the B matrices on a given bond.
Function that updates the B matrices, the bond matrix s between and the bond dimension chi for bond i. The correponding tensor networks look like this:
| --S--B1--B2-- --B1--B2-- | | | | | | theta: U_bond C: U_bond | | | | |
- Parameters
- iint
Bond index; we update the matrices at sites
i-1, i
.- U_bond
Array
The bond operator which we apply to the wave function. We expect labels
'p0', 'p1', 'p0*', 'p1*'
.
- Returns
- trunc_err
TruncationError
The error of the represented state which is introduced by the truncation during this update step.
- trunc_err
-
update_bond_imag
(self, i, U_bond)¶ Update a bond with a (possibly non-unitary) U_bond.
Similar as
update_bond()
; but after the SVD just keep the A, S, B canonical form. In that way, one can sweep left or right without using old singular values, thus preserving the canonical form during imaginary time evolution.- Parameters
- iint
Bond index; we update the matrices at sites
i-1, i
.- U_bond
Array
The bond operator which we apply to the wave function. We expect labels
'p0', 'p1', 'p0*', 'p1*'
.
- Returns
- trunc_err
TruncationError
The error of the represented state which is introduced by the truncation during this update step.
- trunc_err
-
update_imag
(self, N_steps)¶ Perform an update suitable for imaginary time evolution.
Instead of the even/odd brick structure used for ordinary TEBD, we ‘sweep’ from left to right and right to left, similar as DMRG. Thanks to that, we are actually able to preserve the canonical form.
- Parameters
- N_stepsint
The number of steps for which the whole lattice should be updated.
- Returns
- trunc_err
TruncationError
The error of the represented state which is introduced due to the truncation during this sequence of update steps.
- trunc_err
-
update_step
(self, U_idx_dt, odd)¶ Updates either even or odd bonds in unit cell.
Depending on the choice of p, this function updates all even (
E
, odd=False,0) or odd (O
) (odd=True,1) bonds:| - B0 - B1 - B2 - B3 - B4 - B5 - B6 - | | | | | | | | | | |----| |----| |----| | | | E | | E | | E | | | |----| |----| |----| | |----| |----| |----| | | | O | | O | | O | | | |----| |----| |----| |
Note that finite boundary conditions are taken care of by having
Us[0] = None
.- Parameters
- U_idx_dtint
Time step index in
self._U
, evolve withUs[i] = self.U[U_idx_dt][i]
at bond(i-1,i)
.- oddbool/int
Indication of whether to update even (
odd=False,0
) or even (odd=True,1
) sites
- Returns
- trunc_err
TruncationError
The error of the represented state which is introduced due to the truncation during this sequence of update steps.
- trunc_err