Source code for mindquantum.utils.f

# Copyright 2021 Huawei Technologies Co., Ltd
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"""Useful functions."""

import numbers
from functools import lru_cache

import numpy as np

from mindquantum.config.config import _GLOBAL_MAT_VALUE

from .type_value_check import (
    _check_input_type,
    _check_int_type,
    _check_value_should_between_close_set,
    _check_value_should_not_less,
)

__all__ = ['random_circuit', 'mod', 'normalize', 'random_state']


[docs]def random_circuit(n_qubits, gate_num, sd_rate=0.5, ctrl_rate=0.2, seed=None): """ Generate a random circuit. Args: 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)────────●─────── """ # pylint: disable=import-outside-toplevel,cyclic-import from ..core import gates from ..core.circuit import Circuit _check_int_type('n_qubits', n_qubits) _check_int_type('gate_num', gate_num) _check_input_type('sd_rate', float, sd_rate) _check_input_type('ctrl_rate', float, ctrl_rate) if seed is None: seed = np.random.randint(1, 2**23) _check_int_type('seed', seed) _check_value_should_not_less('n_qubits', 1, n_qubits) _check_value_should_not_less('gate_num', 1, gate_num) _check_value_should_between_close_set('sd_rate', 0, 1, sd_rate) _check_value_should_between_close_set('ctrl_rate', 0, 1, ctrl_rate) _check_value_should_between_close_set('seed', 0, 2**32 - 1, seed) if n_qubits == 1: sd_rate = 1 ctrl_rate = 0 single = { 'param': [gates.RX, gates.RY, gates.RZ, gates.PhaseShift], 'non_param': [gates.X, gates.Y, gates.Z, gates.H], } double = {'param': [gates.XX, gates.YY, gates.ZZ], 'non_param': [gates.SWAP]} circuit = Circuit() np.random.seed(seed) qubits = range(n_qubits) for _ in range(gate_num): if n_qubits == 1: q1, q2 = int(qubits[0]), None else: q1, q2 = np.random.choice(qubits, 2, replace=False) q1, q2 = int(q1), int(q2) if np.random.random() < sd_rate: if np.random.random() > ctrl_rate: q2 = None if np.random.random() < 0.5: gate = np.random.choice(single['param']) param = np.random.uniform(-np.pi * 2, np.pi * 2) circuit += gate(param).on(q1, q2) else: gate = np.random.choice(single['non_param']) circuit += gate.on(q1, q2) else: if np.random.random() < 0.75: gate = np.random.choice(double['param']) param = np.random.uniform(-np.pi * 2, np.pi * 2) circuit += gate(param).on([q1, q2]) else: gate = np.random.choice(double['non_param']) circuit += gate.on([q1, q2]) return circuit
def _check_num_array(vec, name): if not isinstance(vec, (np.ndarray, list)): raise TypeError(f"{name} requires a numpy.ndarray or a list of number, but get {type(vec)}.")
[docs]def mod(vec_in, axis=0): """ Calculate the mod of input vectors. Args: vec_in (Union[list[numbers.Number], numpy.ndarray]): The vector you want to calculate mod. axis (int): Along which axis you want to calculate mod. Default: 0. 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]]) """ _check_num_array(vec_in, 'vec_in') vec_in = np.array(vec_in) return np.sqrt(np.sum(np.conj(vec_in) * vec_in, axis=axis, keepdims=True))
[docs]def normalize(vec_in, axis=0): """ Normalize the input vectors based on specified axis. Args: 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]]) """ _check_num_array(vec_in, 'vec_in') vec_in = np.array(vec_in) return vec_in / mod(vec_in, axis=axis)
[docs]def random_state(shapes, norm_axis=0, comp=True, seed=None): r""" Generate some random quantum state. Args: shapes (tuple): shapes = (m, n) means m quantum states with each state formed by :math:`\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]]) """ if not isinstance(shapes, (int, tuple)): raise TypeError(f"shape requires a int of a tuple of int, but get {type(shapes)}!") if not isinstance(comp, bool): raise TypeError(f"comp requires a bool, but get {comp}!") np.random.seed(seed) out = np.random.uniform(size=shapes) + 0j if comp: out += np.random.uniform(size=shapes) * 1j if norm_axis is False: return out return normalize(out, axis=norm_axis)
def is_two_number_close(a, b, atol=None): # pylint: disable=invalid-name """ Check whether two number is close within the error of atol. This method also works for complex numbers. Args: a (numbers.Number): The first number. b (numbers.Number): The second number. atol (float): The atol. If None, the precision defined in global config will be used. Default: None. Returns: bool, whether this two number close to each other. Examples: >>> from mindquantum.utils import is_two_number_close >>> is_two_number_close(1+1j, 1+1j) True """ from mindquantum.config.config import ( # pylint: disable=import-outside-toplevel Context, ) _check_input_type("a", numbers.Number, a) _check_input_type("b", numbers.Number, b) if atol is None: atol = Context.get_precision() _check_input_type("atol", float, atol) return np.allclose(np.abs(a - b), 0, atol=atol) def is_power_of_two(num): """Check whether a number is power of 2 or not.""" return (num & (num - 1) == 0) and num != 0 @lru_cache() def pauli_string_matrix(pauli_string): """ Generate the matrix of pauli string. If pauli string is XYZ, then the matrix will be `Z@Y@X`. """ try: matrix = _GLOBAL_MAT_VALUE[pauli_string[0]] for string in pauli_string[1:]: matrix = np.kron(_GLOBAL_MAT_VALUE[string], matrix) except KeyError as err: raise err return matrix