Merge branch 'main' into relax-pydantic

setup.py

@@ -33,6 +33,7 @@
             "flake8",
             "isort",
             "jsonschema==3.2.0",
+            "numpy",
             "pre-commit",
             "pylint",
             "pytest",

src/braket/ir/jaqcd/__init__.py

@@ -35,6 +35,7 @@
     I,
     ISwap,
     Kraus,
+    MultiQubitPauliChannel,
     PauliChannel,
     PhaseDamping,
     PhaseFlip,

src/braket/ir/jaqcd/instructions.py

@@ -20,6 +20,7 @@
     DampingSingleProbability,
     DoubleControl,
     DoubleTarget,
+    MultiProbability,
     MultiTarget,
     SingleControl,
     SingleProbability,
@@ -766,7 +767,7 @@ class Type(str, Enum):
 
 class PauliChannel(SingleTarget, TripleProbability):
     """
-    Genearal Pauli noise channel.
+    A single qubit Pauli noise channel.
 
     Attributes:
         type (str): The instruction type. default = "pauli_channel". (type) is
@@ -783,6 +784,28 @@ class Type(str, Enum):
     type = Type.pauli_channel
 
 
+class MultiQubitPauliChannel(DoubleTarget, MultiProbability):
+    """
+    Multi-qubit Pauli noise channel.
+
+    Attributes:
+        type (str): The instruction type. default = "multi_qubit_pauli_channel". (type) is
+            optional. This should be unique among all instruction types.
+        target (int): The target qubit(s). This is list of intergers >= 0.
+        The length of the list must match the length of the Pauli strings provided.
+
+    Examples:
+        >>> MultiQubitPauliChannel(target=1, probabilities={"X": 0.1})
+        >>> MultiQubitPauliChannel(target=[0,1], probabilities={"XY": 0.1})
+        >>> MultiQubitPauliChannel(target=[0,1,2], probabilities={"XYZ": 0.1})
+    """
+
+    class Type(str, Enum):
+        multi_qubit_pauli_channel = "multi_qubit_pauli_channel"
+
+    type = Type.multi_qubit_pauli_channel
+
+
 class Depolarizing(SingleTarget, SingleProbability_34):
     """
     Depolarizing noise channel.

src/braket/ir/jaqcd/shared_models.py

@@ -11,7 +11,7 @@
 # ANY KIND, either express or implied. See the License for the specific
 # language governing permissions and limitations under the License.
 
-from typing import Optional, Union
+from typing import Dict, Optional, Union
 
 from pydantic import BaseModel, confloat, conint, conlist, constr, root_validator
 
@@ -233,6 +233,54 @@ def validate_probabilities(cls, values):
         return values
 
 
+class MultiProbability(BaseModel):
+    """A multi-value-probability parameter set for the Pauli noise channel.
+
+    Attributes:
+        probabilities [Dict[str, float]]: The coefficients of the Pauli channel
+
+    Examples:
+        >>> MultiProbability(probabilities={"X": 0.1})
+        >>> MultiProbability(probabilities={"XY": 0.1, "YX": 0.01})
+    """
+
+    probabilities: Dict[
+        constr(regex="^[IXYZ]+$", min_length=1), confloat(ge=float("0.0"), le=float("1.0"))
+    ]
+
+    @root_validator
+    def validate_probabilities(cls, values):
+        """
+        Pydantic uses the validation subsystem to create objects.
+        This custom validator has the purpose to ensure sum(probabilities) <= 1
+        and that the lengths of each Pauli string are equal.
+        """
+
+        probabilities = values.get("probabilities")
+        if not probabilities:
+            raise ValueError("Pauli dictionary must not be empty.")
+
+        qubit_count = len(list(probabilities)[0])
+
+        if qubit_count * "I" in probabilities.keys():
+            i = qubit_count * "I"
+            raise ValueError(
+                f"{i} is not allowed as a key. Please enter only non-identity Pauli strings."
+            )
+
+        for pauli_string, prob in probabilities.items():
+            if len(pauli_string) != qubit_count:
+                raise ValueError("Length of each Pauli string must be equal to number of qubits.")
+
+        total_prob = sum(probabilities.values())
+        if total_prob > 1.0 or total_prob < 0.0:
+            raise ValueError(
+                f"Total probability must be a real number in the interval [0, 1]. Total probability was {total_prob}."  # noqa: E501
+            )
+
+        return values
+
+
 class TwoDimensionalMatrix(BaseModel):
     """
     Two-dimensional non-empty matrix.

test/unit_tests/braket/ir/jaqcd/test_multi_probability.py

@@ -0,0 +1,87 @@
+# Copyright Amazon.com Inc. or its affiliates. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"). You
+# may not use this file except in compliance with the License. A copy of
+# the License is located at
+#
+#     http://aws.amazon.com/apache2.0/
+#
+# or in the "license" file accompanying this file. This file 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 limitations under the License.
+
+from itertools import product
+
+import numpy as np
+import pytest
+
+from braket.ir.jaqcd.shared_models import MultiProbability
+
+np.random.seed(1)
+
+
+def pauli_strings(n_qubits: int):
+    pauli_strings = list(map(lambda x: "".join(x), product(["I", "X", "Y", "Z"], repeat=n_qubits)))
+    return pauli_strings[1:]  # remove identity term
+
+
+def random_noise(n_qubits: int):
+    return np.random.dirichlet(np.ones(4**n_qubits))[1:]  # remove identity term
+
+
+@pytest.mark.parametrize("n_qubits", [1, 2, 3])
+class TestMultiProbability:
+    """A class with common parameters, `param1` and `param2`."""
+
+    def test_multiprobability(self, n_qubits):
+        paulis = dict(zip(pauli_strings(n_qubits), random_noise(n_qubits)))
+        MultiProbability(probabilities=paulis)
+
+    def test_multiprobability_less_probabilities(self, n_qubits):
+        paulis = dict(zip(pauli_strings(n_qubits), random_noise(n_qubits)))
+        del paulis["X" * n_qubits]
+        del paulis["Z" * n_qubits]
+        MultiProbability(probabilities=paulis)
+
+    @pytest.mark.xfail(raises=ValueError)
+    @pytest.mark.parametrize("str", ["T", "s", "x"])
+    def test_multiprobability_non_pauli(self, n_qubits, str):
+        paulis = dict(zip(pauli_strings(n_qubits), random_noise(n_qubits)))
+        paulis[str * n_qubits] = 0.0
+        MultiProbability(probabilities=paulis)
+
+    @pytest.mark.xfail(raises=ValueError)
+    @pytest.mark.parametrize("value", [12, -0.1, 1.1, np.inf, None])
+    def test_multiprobability_non_float(self, n_qubits, value):
+        paulis = dict(zip(pauli_strings(n_qubits), random_noise(n_qubits)))
+        paulis["X" * n_qubits] = value
+        MultiProbability(probabilities=paulis)
+
+    @pytest.mark.xfail(raises=ValueError)
+    def test_multiprobability_empty(self, n_qubits):
+        MultiProbability(probabilities={})
+
+    @pytest.mark.xfail(raises=ValueError)
+    def test_multiprobability_identity(self, n_qubits):
+        paulis = dict(zip(pauli_strings(n_qubits), random_noise(n_qubits)))
+        paulis["I" * n_qubits] = 0.1
+        MultiProbability(probabilities=paulis)
+
+    @pytest.mark.xfail(raises=ValueError)
+    def test_multiprobability_pauli_equal_lengths(self, n_qubits):
+        paulis = dict(zip(pauli_strings(n_qubits), random_noise(n_qubits)))
+        paulis["X"] = 0.0
+        paulis["XY"] = 0.0
+        MultiProbability(probabilities=paulis)
+
+    @pytest.mark.xfail(raises=ValueError)
+    def test_multiprobability_sum_over_one(self, n_qubits):
+        paulis = dict(zip(pauli_strings(n_qubits), 10 * random_noise(n_qubits)))
+        MultiProbability(probabilities=paulis)
+
+    @pytest.mark.xfail(raises=ValueError)
+    def test_multiprobability_sum_under_zero(self, n_qubits):
+        paulis = dict(zip(pauli_strings(n_qubits), random_noise(n_qubits)))
+        paulis["Z" * n_qubits] = -0.9
+        MultiProbability(probabilities=paulis)