# spectral_range¶

Graph.spectral_range(operator, iteration_count=3000, seed=None, *, name=None)

Obtains the range of the eigenvalues of a Hermitian operator.

This function provides an estimate of the difference between the highest and the lowest eigenvalues of the operator. You can adjust its precision by modifying its default parameters.

Parameters
• operator (np.ndarray or scipy.sparse.coo_matrix or Tensor) – The Hermitian operator $$M$$ whose range of eigenvalues you want to determine.

• iteration_count (int, optional) – The number of iterations $$N$$ in the calculation. Defaults to 3000. Choose a higher number to improve the precision, or a smaller number to make the estimation run faster.

• seed (int, optional) – The random seed that the function uses to choose the initial random vector $$\left| r \right\rangle$$. Defaults to None, which means that the function uses a different seed in each run.

• name (str, optional) – The name of the node.

Returns

The difference between the largest and the smallest eigenvalues of the operator.

Return type

Tensor (scalar, real)

Warning

This calculation can be expensive, so we recommend that you run it before the optimization, if possible. You can do this by using a representative or a worst-case operator.

Notes

This function repeatedly multiplies the operator $$M$$ with a random vector $$\left| r \right\rangle$$. In terms of the operator’s eigenvalues $$\{ v_i \}$$ and eigenvectors $$\{\left|v_i \right\rangle\}$$, the result of $$N$$ matrix multiplications is:

$M^N \left|r\right\rangle = \sum_i v_i^N \left|v_i\right\rangle \left\langle v_i \right. \left| r \right\rangle.$

For large $$N$$, the term corresponding to the eigenvalue with largest absolute value $$V$$ will dominate the sum, as long as $$\left|r\right\rangle$$ has a non-zero overlap with its eigenvector. The function then retrieves the eigenvalue $$V$$ via:

$V \approx \frac{\left\langle r \right| M^{2N+1} \left| r \right\rangle}{\left\| M^N \left| r \right\rangle \right\|^2}.$

The same procedure applied to the matrix $$M-V$$ allows the function to find the eigenvalue at the opposite end of the spectral range.