mindquantum.utils

Utils.

mindquantum.utils.fdopen(fname, mode, perms=420, encoding=None)[source]

Context manager for opening files with correct permissions.

Parameters

  • fname (str) – Path to file to open for reading/writing

  • mode (str) – Mode in which the file is opened (see help for builtin open())

  • perms (int) – Permission mask (see help for os.open())

  • encoding (str) – The name of encoding used to decode or encode the file.

mindquantum.utils.ket_string(state, tol=1e-07)[source]

Get the ket format of the quantum state.

Parameters

  • state (numpy.ndarray) – The input quantum state.

  • tol (float) – The ignore tolerance for small amplitude. Default: 1e-7.

Returns

str, the ket format of the quantum state.

Examples

>>> import numpy as np
>>> from mindquantum.utils import ket_string
>>> state = np.array([1, -1j])/np.sqrt(2)
>>> print(ket_string(state))
['√2/2¦0⟩', '-√2/2j¦1⟩']
mindquantum.utils.mod(vec_in, axis=0)[source]

Calculate the mod of input vectors.

Parameters
Returns

numpy.ndarray, The mod of input vector.

Examples

>>> from mindquantum.utils import mod
>>> 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]])
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. Default: 0.

Returns

numpy.ndarray, Vector after normalization.

Examples

>>> from mindquantum.utils import normalize
>>> 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]])
mindquantum.utils.random_circuit(n_qubits, gate_num, sd_rate=0.5, ctrl_rate=0.2, seed=None)[source]

Generate a random circuit.

Parameters

  • n_qubits (int) – Number of qubits of random circuit.

  • gate_num (int) – Number of gates in random circuit.

  • sd_rate (float) – The rate of single qubit gate and double qubits gates.

  • ctrl_rate (float) – The possibility that a gate has a control qubit.

  • seed (int) – Random seed to generate random circuit.

Examples

>>> from mindquantum.utils import random_circuit
>>> random_circuit(3, 4, 0.5, 0.5, 100)
q1: ──Z────RX(0.944)────────●────────RX(-0.858)──
      │        │            │            │
q2: ──●────────●────────RZ(-2.42)────────●───────
mindquantum.utils.random_state(shapes, norm_axis=0, comp=True, seed=None)[source]

Generate some random quantum state.

Parameters

  • shapes (tuple) – shapes = (m, n) means m quantum states with each state formed by \(\log_2(n)\) qubits.

  • norm_axis (int) – which axis you want to apply normalization. Default: 0.

  • comp (bool) – if True, each amplitude of the quantum state will be a complex number. Default: True.

  • seed (int) – the random seed. Default: None.

Returns

numpy.ndarray, A normalized random quantum state.

Examples

>>> from mindquantum.utils import random_state
>>> random_state((2, 2), seed=42)
array([[0.44644744+0.18597239j, 0.66614846+0.10930256j],
       [0.87252821+0.06923499j, 0.41946926+0.60691409j]])