feat: add amplitude damping noise model (#497)

graphix/__init__.py

@@ -13,7 +13,7 @@
 from graphix.graphsim import GraphState
 from graphix.instruction import Instruction
 from graphix.measurements import BlochMeasurement, Measurement, PauliMeasurement
-from graphix.noise_models import DepolarisingNoiseModel, NoiseModel
+from graphix.noise_models import AmplitudeDampingNoiseModel, DepolarisingNoiseModel, NoiseModel
 from graphix.opengraph import OpenGraph
 from graphix.optimization import StandardizedPattern
 from graphix.parameter import Placeholder
@@ -27,6 +27,7 @@
 
 __all__ = [
     "ANGLE_PI",
+    "AmplitudeDampingNoiseModel",
     "Axis",
     "BasicStates",
     "BlochMeasurement",

graphix/channels.py

@@ -200,6 +200,76 @@ def depolarising_channel(prob: float) -> KrausChannel:
     )
 
 
+def amplitude_damping_channel(gamma: float) -> KrausChannel:
+    r"""Single-qubit amplitude damping channel.
+
+    .. math::
+        K_1 = \begin{pmatrix}
+                1 & 0 \\
+                0 & \sqrt{1-\gamma}
+              \end{pmatrix},\quad
+        K_2 = \begin{pmatrix}
+                0 & \sqrt{\gamma} \\
+                0 & 0
+              \end{pmatrix}
+
+    Parameters
+    ----------
+    gamma : float
+        The damping probability, between 0 and 1.
+
+    Returns
+    -------
+    :class:`graphix.channels.KrausChannel` object
+        containing the corresponding Kraus operators
+    """
+    if not 0 <= gamma <= 1:
+        raise ValueError("gamma must be between 0 and 1.")
+    sqrt_gamma = np.sqrt(gamma)
+    sqrt_one_minus_gamma = np.sqrt(1 - gamma)
+    return KrausChannel(
+        [
+            KrausData(1.0, np.array([[1, 0], [0, sqrt_one_minus_gamma]], dtype=np.complex128)),
+            KrausData(1.0, np.array([[0, sqrt_gamma], [0, 0]], dtype=np.complex128)),
+        ]
+    )
+
+
+def two_qubit_amplitude_damping_channel(gamma: float) -> KrausChannel:
+    r"""Two-qubit amplitude damping channel (independent tensor product).
+
+    The two-qubit channel is formed by the tensor product of two single-qubit
+    amplitude damping channels, yielding 4 Kraus operators:
+
+    .. math::
+        \{K_1 \otimes K_1,\; K_1 \otimes K_2,\; K_2 \otimes K_1,\; K_2 \otimes K_2\}
+
+    Parameters
+    ----------
+    gamma : float
+        The damping probability, between 0 and 1.
+
+    Returns
+    -------
+    :class:`graphix.channels.KrausChannel` object
+        containing the corresponding Kraus operators
+    """
+    if not 0 <= gamma <= 1:
+        raise ValueError("gamma must be between 0 and 1.")
+    sqrt_gamma = np.sqrt(gamma)
+    sqrt_one_minus_gamma = np.sqrt(1 - gamma)
+    k1 = np.array([[1, 0], [0, sqrt_one_minus_gamma]], dtype=np.complex128)
+    k2 = np.array([[0, sqrt_gamma], [0, 0]], dtype=np.complex128)
+    return KrausChannel(
+        [
+            KrausData(1.0, np.kron(k1, k1)),
+            KrausData(1.0, np.kron(k1, k2)),
+            KrausData(1.0, np.kron(k2, k1)),
+            KrausData(1.0, np.kron(k2, k2)),
+        ]
+    )
+
+
 def pauli_channel(px: float, py: float, pz: float) -> KrausChannel:
     r"""Single-qubit Pauli channel.
 

graphix/noise_models/__init__.py

@@ -4,6 +4,11 @@
 
 from typing import TYPE_CHECKING
 
+from graphix.noise_models.amplitude_damping import (
+    AmplitudeDampingNoise,
+    AmplitudeDampingNoiseModel,
+    TwoQubitAmplitudeDampingNoise,
+)
 from graphix.noise_models.depolarising import DepolarisingNoise, DepolarisingNoiseModel, TwoQubitDepolarisingNoise
 from graphix.noise_models.noise_model import (
     ApplyNoise,
@@ -16,11 +21,14 @@
     from graphix.noise_models.noise_model import CommandOrNoise as CommandOrNoise
 
 __all__ = [
+    "AmplitudeDampingNoise",
+    "AmplitudeDampingNoiseModel",
     "ApplyNoise",
     "ComposeNoiseModel",
     "DepolarisingNoise",
     "DepolarisingNoiseModel",
     "Noise",
     "NoiseModel",
+    "TwoQubitAmplitudeDampingNoise",
     "TwoQubitDepolarisingNoise",
 ]

graphix/noise_models/amplitude_damping.py

@@ -0,0 +1,156 @@
+"""Amplitude damping noise model."""
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import typing_extensions
+
+from graphix.channels import amplitude_damping_channel, two_qubit_amplitude_damping_channel
+from graphix.command import BaseM, CommandKind
+from graphix.measurements import toggle_outcome
+from graphix.noise_models.noise_model import ApplyNoise, Noise, NoiseModel
+from graphix.rng import ensure_rng
+from graphix.utils import Probability
+
+if TYPE_CHECKING:
+    from collections.abc import Iterable
+
+    from numpy.random import Generator
+
+    from graphix.channels import KrausChannel
+    from graphix.measurements import Outcome
+    from graphix.noise_models.noise_model import CommandOrNoise
+
+
+class AmplitudeDampingNoise(Noise):
+    """One-qubit amplitude damping noise with damping parameter ``gamma``."""
+
+    gamma = Probability()
+
+    def __init__(self, gamma: float) -> None:
+        """Initialize one-qubit amplitude damping noise.
+
+        Parameters
+        ----------
+        gamma : float
+            Damping parameter of the noise, between 0 and 1.
+        """
+        self.gamma = gamma
+
+    @property
+    @typing_extensions.override
+    def nqubits(self) -> int:
+        """Return the number of qubits targetted by the noise element."""
+        return 1
+
+    @typing_extensions.override
+    def to_kraus_channel(self) -> KrausChannel:
+        """Return the Kraus channel describing the noise element."""
+        return amplitude_damping_channel(self.gamma)
+
+
+class TwoQubitAmplitudeDampingNoise(Noise):
+    """Two-qubit amplitude damping noise with damping parameter ``gamma``."""
+
+    gamma = Probability()
+
+    def __init__(self, gamma: float) -> None:
+        """Initialize two-qubit amplitude damping noise.
+
+        Parameters
+        ----------
+        gamma : float
+            Damping parameter of the noise, between 0 and 1.
+        """
+        self.gamma = gamma
+
+    @property
+    @typing_extensions.override
+    def nqubits(self) -> int:
+        """Return the number of qubits targetted by the noise element."""
+        return 2
+
+    @typing_extensions.override
+    def to_kraus_channel(self) -> KrausChannel:
+        """Return the Kraus channel describing the noise element."""
+        return two_qubit_amplitude_damping_channel(self.gamma)
+
+
+class AmplitudeDampingNoiseModel(NoiseModel):
+    """Amplitude damping noise model.
+
+    :param NoiseModel: Parent abstract class :class:`NoiseModel`
+    :type NoiseModel: class
+    """
+
+    def __init__(
+        self,
+        prepare_error_prob: float = 0.0,
+        x_error_prob: float = 0.0,
+        z_error_prob: float = 0.0,
+        entanglement_error_prob: float = 0.0,
+        measure_channel_prob: float = 0.0,
+        measure_error_prob: float = 0.0,
+    ) -> None:
+        self.prepare_error_prob = prepare_error_prob
+        self.x_error_prob = x_error_prob
+        self.z_error_prob = z_error_prob
+        self.entanglement_error_prob = entanglement_error_prob
+        self.measure_error_prob = measure_error_prob
+        self.measure_channel_prob = measure_channel_prob
+
+    @typing_extensions.override
+    def input_nodes(
+        self, nodes: Iterable[int], rng: Generator | None = None, *, stacklevel: int = 1
+    ) -> list[CommandOrNoise]:
+        """Return the noise to apply to input nodes."""
+        return [ApplyNoise(noise=AmplitudeDampingNoise(self.prepare_error_prob), nodes=[node]) for node in nodes]
+
+    @typing_extensions.override
+    def command(
+        self, cmd: CommandOrNoise, rng: Generator | None = None, *, stacklevel: int = 1
+    ) -> list[CommandOrNoise]:
+        """Return the noise to apply to the command ``cmd``."""
+        match cmd.kind:
+            case CommandKind.N:
+                return [
+                    cmd,
+                    ApplyNoise(noise=AmplitudeDampingNoise(self.prepare_error_prob), nodes=[cmd.node]),
+                ]
+            case CommandKind.E:
+                return [
+                    cmd,
+                    ApplyNoise(
+                        noise=TwoQubitAmplitudeDampingNoise(self.entanglement_error_prob),
+                        nodes=list(cmd.nodes),
+                    ),
+                ]
+            case CommandKind.M:
+                return [ApplyNoise(noise=AmplitudeDampingNoise(self.measure_channel_prob), nodes=[cmd.node]), cmd]
+            case CommandKind.X:
+                return [
+                    cmd,
+                    ApplyNoise(noise=AmplitudeDampingNoise(self.x_error_prob), nodes=[cmd.node], domain=cmd.domain),
+                ]
+            case CommandKind.Z:
+                return [
+                    cmd,
+                    ApplyNoise(noise=AmplitudeDampingNoise(self.z_error_prob), nodes=[cmd.node], domain=cmd.domain),
+                ]
+            case CommandKind.C | CommandKind.T | CommandKind.ApplyNoise:
+                return [cmd]
+            case CommandKind.S:
+                raise ValueError("Unexpected signal!")
+            case _:
+                typing_extensions.assert_never(cmd.kind)
+
+    @typing_extensions.override
+    def confuse_result(
+        self, cmd: BaseM, result: Outcome, rng: Generator | None = None, *, stacklevel: int = 1
+    ) -> Outcome:
+        """Assign wrong measurement result cmd = "M"."""
+        rng = ensure_rng(rng, stacklevel=stacklevel + 1)
+        if rng.uniform() < self.measure_error_prob:
+            return toggle_outcome(result)
+        return result

tests/test_kraus.py

@@ -9,8 +9,10 @@
 from graphix.channels import (
     KrausChannel,
     KrausData,
+    amplitude_damping_channel,
     dephasing_channel,
     depolarising_channel,
+    two_qubit_amplitude_damping_channel,
     two_qubit_depolarising_channel,
     two_qubit_depolarising_tensor_channel,
 )
@@ -180,3 +182,49 @@ def test_2_qubit_depolarising_tensor_channel(self, fx_rng: Generator) -> None:
         for i in range(len(depol_tensor_channel_2_qubit)):
             assert np.allclose(depol_tensor_channel_2_qubit[i].coef, data[i].coef)
             assert np.allclose(depol_tensor_channel_2_qubit[i].operator, data[i].operator)
+
+    def test_amplitude_damping_channel(self, fx_rng: Generator) -> None:
+        gamma = fx_rng.uniform(0, 0.5)
+        sqrt_gamma = np.sqrt(gamma)
+        sqrt_one_minus_gamma = np.sqrt(1 - gamma)
+        data_k1 = np.array([[1, 0], [0, sqrt_one_minus_gamma]], dtype=np.complex128)
+        data_k2 = np.array([[0, sqrt_gamma], [0, 0]], dtype=np.complex128)
+        ad_channel = amplitude_damping_channel(gamma)
+        assert isinstance(ad_channel, KrausChannel)
+        assert ad_channel.nqubit == 1
+        assert len(ad_channel) == 2
+        assert np.allclose(ad_channel[0].operator, data_k1)
+        assert np.allclose(ad_channel[0].coef, 1.0)
+        assert np.allclose(ad_channel[1].operator, data_k2)
+        assert np.allclose(ad_channel[1].coef, 1.0)
+
+    def test_two_qubit_amplitude_damping_channel(self, fx_rng: Generator) -> None:
+        gamma = fx_rng.uniform(0, 0.5)
+        sqrt_gamma = np.sqrt(gamma)
+        sqrt_one_minus_gamma = np.sqrt(1 - gamma)
+        k1 = np.array([[1, 0], [0, sqrt_one_minus_gamma]], dtype=np.complex128)
+        k2 = np.array([[0, sqrt_gamma], [0, 0]], dtype=np.complex128)
+        ad2_channel = two_qubit_amplitude_damping_channel(gamma)
+        assert isinstance(ad2_channel, KrausChannel)
+        assert ad2_channel.nqubit == 2
+        assert len(ad2_channel) == 4
+        expected = [np.kron(k1, k1), np.kron(k1, k2), np.kron(k2, k1), np.kron(k2, k2)]
+        for i, exp in enumerate(expected):
+            assert np.allclose(ad2_channel[i].operator, exp)
+            assert np.allclose(ad2_channel[i].coef, 1.0)
+
+    def test_amplitude_damping_channel_edge_cases(self) -> None:
+        # gamma=0 → only K1 (identity)
+        c0 = amplitude_damping_channel(0.0)
+        assert np.allclose(c0[0].operator, np.eye(2))
+        assert np.allclose(c0[0].coef, 1.0)
+        assert len(c0) == 2
+        # gamma=1 → K1 = |0><0|, K2 = |0><1|
+        c1 = amplitude_damping_channel(1.0)
+        assert np.allclose(c1[0].operator, np.array([[1, 0], [0, 0]]))
+        assert np.allclose(c1[1].operator, np.array([[0, 1], [0, 0]]))
+        # invalid gamma
+        with pytest.raises(ValueError):
+            amplitude_damping_channel(-0.1)
+        with pytest.raises(ValueError):
+            amplitude_damping_channel(1.1)