mindquantum.utils¶
Utils
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mindquantum.utils.
bprint
(strings: list, align=":", title="", v_around="=", h_around="|", fill_char=" ")[source]¶ Print the information in block shape.
- Parameters
- Returns
list(str), Formatted string.
Examples
>>> title='Info of Bob' >>> o = bprint(['Name:Bob', 'Age:17', 'Nationality:China'], >>> title=title) >>> for i in o: >>> print(i) ====Info of Bob==== |Name :Bob | |Age :17 | |Nationality:China| ===================
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mindquantum.utils.
mod
(vec_in, axis=0)[source]¶ Calculate the mod of input vectors.
- Parameters
vec_in (Union[list[number], numpy.ndarray]) – The vector you want to calculate mod.
axis (int) – Along which axis you want to calculate mod.
- Returns
numpy.ndarray, The mod of input vector.
Examples
>>> vec_in = np.array([[1, 2, 3], [4, 5, 6]]) >>> mod(vec_in) array([[4.12310563, 5.38516481, 6.70820393]]) >>> mod(vec_in, 1) array([[3.74165739], [8.77496439]])
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mindquantum.utils.
normalize
(vec_in, axis=0)[source]¶ Normalize the input vectors based on specified axis.
- Parameters
vec_in (Union[list[number], numpy.ndarray]) – Vector you want to normalize.
axis (int) – Along which axis you want to normalize your vector.
- Returns
numpy.ndarray, Vector after normalization.
Examples
>>> vec_in = np.array([[1, 2, 3], [4, 5, 6]]) >>> normalize(vec_in) array([[0.24253563, 0.37139068, 0.4472136 ], [0.9701425 , 0.92847669, 0.89442719]]) >>> normalize(vec_in, 1) array([[0.26726124, 0.53452248, 0.80178373], [0.45584231, 0.56980288, 0.68376346]])
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mindquantum.utils.
random_state
(shapes, norm_axis=0, comp=True, seed=None)[source]¶ Generate some random quantum state.
- Parameters
- Returns
numpy.ndarray, A normalized random quantum state.
Examples
>>> random_state((2, 2), seed=42) array([[0.44644744+0.18597239j, 0.66614846+0.10930256j], [0.87252821+0.06923499j, 0.41946926+0.60691409j]])