RandomUnitaryEvolution¶
full name: tenpy.algorithms.tebd.RandomUnitaryEvolution
parent module:
tenpy.algorithms.tebd
type: class
Inheritance Diagram
Methods
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Initialize self. |
Draw new random two-site unitaries replacing the usual U of TEBD. |
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Time evolution with TEBD and random two-site unitaries. |
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TEBD algorithm in imaginary time to find the ground state. |
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Returns list of necessary steps for the suzuki trotter decomposition. |
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Return time steps of U for the Suzuki Trotter decomposition of desired order. |
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Apply |
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Updates the B matrices on a given bond. |
Update a bond with a (possibly non-unitary) U_bond. |
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Perform an update suitable for imaginary time evolution. |
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Updates either even or odd bonds in unit cell. |
Class Attributes and Properties
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truncation error introduced on each non-trivial bond. |
-
class
tenpy.algorithms.tebd.
RandomUnitaryEvolution
(psi, options)[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
Options
-
config
RandomUnitaryEvolution
¶ option summary delta_tau_list (from TEBD) in PurificationTEBD.run_GS
A list of floats: the timesteps to be used. [...]
Time step.
Number of two-site unitaries to be applied on each bond.
Order of the algorithm. The total error scales as ``O(t*dt^order)``.
start_time (from TEBD) in Engine
Initial value for :attr:`evolved_time`.
start_trunc_err (from TEBD) in Engine
Initial truncation error for :attr:`trunc_err`.
Truncation parameters as described in :cfg:config:`truncate`
How much to print what's being done; higher means print more. [...]
-
option
N_steps
: int¶ Number of two-site unitaries to be applied on each bond.
-
option
trunc_params
: dict¶ Truncation parameters as described in
truncate
-
option
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] >>> options = dict(N_steps=2, trunc_params={'chi_max':10}) >>> eng = RandomUnitaryEvolution(psi, options) >>> 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, options) >>> 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]
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update
(N_steps)[source]¶ Apply
N_steps
random two-site unitaries to each bond (in even-odd pattern).- Parameters
N_steps (int) – The number of steps for which the whole lattice should be updated.
- Returns
trunc_err – The error of the represented state which is introduced due to the truncation during this sequence of update steps.
- Return type
-
run_GS
()[source]¶ 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.
-
option
TEBD
.
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.
-
option
-
static
suzuki_trotter_decomposition
(order, N_steps)[source]¶ 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
order (int) – The desired order of the Suzuki-Trotter decomposition.
- Returns
ST_decomposition – 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>
.- Return type
-
static
suzuki_trotter_time_steps
(order)[source]¶ Return time steps of U for the Suzuki Trotter decomposition of desired order.
See
suzuki_trotter_decomposition()
for details.- Parameters
order (int) – The desired order of the Suzuki-Trotter decomposition.
- Returns
time_steps – We need
U = exp(-i H_{even/odd} delta_t * dt)
for the dt returned in this list.- Return type
list of float
-
property
trunc_err_bonds
¶ truncation error introduced on each non-trivial bond.
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update_bond
(i, U_bond)[source]¶ 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
- Returns
trunc_err – The error of the represented state which is introduced by the truncation during this update step.
- Return type
-
update_bond_imag
(i, U_bond)[source]¶ 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
- Returns
trunc_err – The error of the represented state which is introduced by the truncation during this update step.
- Return type
-
update_imag
(N_steps)[source]¶ 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_steps (int) – The number of steps for which the whole lattice should be updated.
- Returns
trunc_err – The error of the represented state which is introduced due to the truncation during this sequence of update steps.
- Return type
-
update_step
(U_idx_dt, odd)[source]¶ 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_dt (int) – Time step index in
self._U
, evolve withUs[i] = self.U[U_idx_dt][i]
at bond(i-1,i)
.odd (bool/int) – Indication of whether to update even (
odd=False,0
) or even (odd=True,1
) sites
- Returns
trunc_err – The error of the represented state which is introduced due to the truncation during this sequence of update steps.
- Return type