network_operator_append_product#

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

Appends a tensor product operator 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_tensors (int32_t) – Number of tensor factors in the tensor product.
num_state_modes (object) –
Number of state modes each appended tensor factor acts on. It can be:
- an int as the pointer address to the array, or
- a Python sequence of int32_t.
state_modes (object) –
Modes each appended tensor factor acts on (length = num_state_modes). 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 ‘int32_t’, or
- a nested Python sequence of int32_t.
tensor_mode_strides (object) –
Tensor mode strides for each tensor factor (length = num_state_modes * 2). If NULL, the default generalized column-major strides will be used. 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 tensor factor. It can be:
- an int as the pointer address to the array, or
- a Python sequence of ints (as pointer addresses).

Returns:

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

Return type:

int64_t