# sparse_pwc_operator¶

Graph.sparse_pwc_operator(signal, operator)

Create a sparse piecewise-constant operator (sparse-matrix-valued function of time).

Each of the piecewise-constant segments (time periods) is a scalar multiple of the operator.

Parameters
• signal (Pwc) – The scalar-valued piecewise-constant function of time $$a(t)$$.

• operator (numpy.ndarray or scipy.sparse.spmatrix) – The sparse operator $$A$$ to be scaled over time. If you pass a NumPy array then it will be internally converted into a sparse array.

Returns

The piecewise-constant sparse operator $$a(t)A$$.

Return type

SparsePwc

constant_sparse_pwc_operator()

Create constant SparsePwcs.

density_matrix_evolution_pwc()

Evolve a quantum state in an open system.

pwc_operator()

Corresponding operation for Pwcs.

sparse_pwc_hermitian_part()

Hermitian part of a SparsePwc operator.

sparse_pwc_sum()

Sum multiple SparsePwcs.

state_evolution_pwc()

Evolve a quantum state.

Examples

Create a sparse PWC operator.

>>> from scipy.sparse import coo_matrix
>>> sigma_x = np.array([[0, 1], [1, 0]])
>>> signal = graph.pwc_signal(values=np.array([1, 2, 3]), duration=0.1)
>>> graph.sparse_pwc_operator(signal=signal, operator=coo_matrix(sigma_x))
<SparsePwc: operation_name="sparse_pwc_operator", value_shape=(2, 2)>


See more examples in the How to simulate large open system dynamics user guide.