network_operator_append_mpo#

cuquantum.bindings.cutensornet.network_operator_append_mpo( intptr_t handle, intptr_t tensor_network_operator, double complex coefficient, int32_t num_state_modes, state_modes, tensor_mode_extents, tensor_mode_strides, tensor_data, int boundary_condition, ) → int64_t[source]#

Appends a Matrix Product Operator (MPO) component to the tensor network operator.

Parameters:

handle (intptr_t) – cuTensorNet library handle.
tensor_network_operator (intptr_t) – Tensor network operator.
coefficient (complex) – Complex coefficient associated with the appended operator component.
num_state_modes (int32_t) – Number of state modes the MPO acts on (number of tensors in the MPO).
state_modes (object) –
State modes the MPO acts on. It can be:
- an int as the pointer address to the array, or
- a Python sequence of int32_t.
tensor_mode_extents (object) –
Tensor mode extents for each MPO tensor. It can be:
- an int as the pointer address to the nested sequence, or
- a Python sequence of ints, each of which is a pointer address to a valid sequence of ‘int64_t’, or
- a nested Python sequence of int64_t.
tensor_mode_strides (object) –
Storage strides for each MPO tensor or NULL (default generalized column-wise strides). It can be:
- an int as the pointer address to the nested sequence, or
- a Python sequence of ints, each of which is a pointer address to a valid sequence of ‘int64_t’, or
- a nested Python sequence of int64_t.
tensor_data (object) –
Tensor data stored in GPU memory for each MPO tensor factor. It can be:
- an int as the pointer address to the array, or
- a Python sequence of ints (as pointer addresses).
boundary_condition (BoundaryCondition) – MPO boundary condition.

Returns:

Unique sequential integer identifier of the appended tensor network operator component.

Return type:

int64_t