Source code for qilisdk.noise.dephasing

# Copyright 2026 Qilimanjaro Quantum Tech
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
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from __future__ import annotations

import numpy as np

from qilisdk.core import QTensor

from .noise import Noise
from .protocols import AttachmentScope, HasAllowedScopes, SupportsStaticLindblad, SupportsTimeDerivedKraus
from .representations import KrausChannel, LindbladGenerator
from .utils import _identity, _sigma_z




class Dephasing(Noise, SupportsTimeDerivedKraus, SupportsStaticLindblad, HasAllowedScopes):
    """Pure dephasing (Tphi) noise model for single qubits.

    This model supports both Lindblad and time-derived Kraus forms, with
    coherences decaying as exp(-t / Tphi).
    """

    def __init__(self, *, t_phi: float) -> None:
        """Args:
            t_phi (float): Dephasing time constant (must be > 0).

        Raises:
            ValueError: If t_phi is not positive.
        """
        if t_phi <= 0:
            raise ValueError("t_phi must be > 0.")
        self._t_phi = float(t_phi)

    @property


    def t_phi(self) -> float:
        """Return the dephasing time constant.

        Returns:
            The t_phi value.
        """
        return self._t_phi




    def as_lindblad(self) -> LindbladGenerator:
        gamma = 1.0 / self._t_phi
        rate = gamma / 2.0
        L = QTensor(_sigma_z())
        return LindbladGenerator(jump_operators=[L], rates=[rate])




    def as_kraus_from_duration(self, *, duration: float) -> KrausChannel:
        if duration < 0:
            raise ValueError("duration must be >= 0.")
        gamma = float(np.exp(-duration / self._t_phi))
        K0 = np.sqrt((1.0 + gamma) / 2.0) * _identity()
        K1 = np.sqrt((1.0 - gamma) / 2.0) * _sigma_z()
        return KrausChannel([QTensor(K0), QTensor(K1)])


    @classmethod


    def allowed_scopes(cls) -> frozenset[AttachmentScope]:
        return frozenset({AttachmentScope.GLOBAL, AttachmentScope.PER_QUBIT})


    def __repr__(self) -> str:
        return f"{type(self).__qualname__}(t_phi={self._t_phi})"