Merge remote-tracking branch 'origin/main' into guyi/AddPagination

.ado/ci.yml

@@ -58,7 +58,7 @@ jobs:
       displayName: Set Python version
 
     - script: |
-        pip install pytest pytest-azurepipelines pytest-cov
+        pip install pytest pytest-azurepipelines pytest-cov pytest-regressions
       displayName: Install pytest dependencies
 
     - script: |

.ado/publish.yml

@@ -108,7 +108,7 @@ extends:
           displayName: Set Python version
 
         - script: |
-            pip install pytest pytest-azurepipelines pytest-cov
+            pip install pytest pytest-azurepipelines pytest-cov pytest-regressions
           displayName: Install pytest dependencies
 
         - script: |

azure-quantum/azure/quantum/argument_types/__init__.py

@@ -3,7 +3,7 @@
 # Licensed under the MIT License.
 ##
 
-"""Defines argument types for Microsoft Estimator"""
+"""Defines argument types for QIR"""
 
 from .types import EmptyArray, Pauli, Range, Result
 

azure-quantum/azure/quantum/qiskit/backends/__init__.py

@@ -37,11 +37,6 @@
     QCIQPUBackend,
 )
 
-from azure.quantum.qiskit.backends.microsoft import (
-    MicrosoftBackend,
-    MicrosoftResourceEstimationBackend,
-)
-
 from .backend import AzureBackendBase
 
 __all__ = [

azure-quantum/azure/quantum/qiskit/job.py

@@ -19,7 +19,6 @@
 import json
 import re
 from azure.quantum import Job
-from azure.quantum.qiskit.results.resource_estimator import make_estimator_result
 
 import logging
 logger = logging.getLogger(__name__)
@@ -38,7 +37,6 @@
 MICROSOFT_OUTPUT_DATA_FORMAT_V2 = "microsoft.quantum-results.v2"
 IONQ_OUTPUT_DATA_FORMAT = "ionq.quantum-results.v1"
 QUANTINUUM_OUTPUT_DATA_FORMAT = "honeywell.quantum-results.v1"
-RESOURCE_ESTIMATOR_OUTPUT_DATA_FORMAT = "microsoft.resource-estimates.v1"
 
 class AzureQuantumJob(JobV1):
     def __init__(
@@ -96,10 +94,7 @@ def result(self, timeout=None, sampler_seed=None):
             "error_data" : None if self._azure_job.details.error_data is None else self._azure_job.details.error_data.as_dict()
         }
 
-        if self._azure_job.details.output_data_format == RESOURCE_ESTIMATOR_OUTPUT_DATA_FORMAT:
-            return make_estimator_result(result_dict)
-        else:
-            return Result.from_dict(result_dict)
+        return Result.from_dict(result_dict)
 
     def cancel(self):
         """Attempt to cancel the job."""

azure-quantum/azure/quantum/target/microsoft/elements/dft/job.py

@@ -1,8 +1,13 @@
 import collections.abc
-from typing import Any, Dict, Union
+import logging
+from typing import Any, Dict, Union, Optional
 from azure.quantum.job import JobFailedWithResultsError
+from azure.quantum.job.base_job import BaseJob, ContentType
 from azure.quantum.job.job import Job, DEFAULT_TIMEOUT
 from azure.quantum._client.models import JobDetails
+from azure.quantum.workspace import Workspace
+
+logger = logging.getLogger(__name__)
 
 class MicrosoftElementsDftJob(Job):
     """
@@ -62,4 +67,105 @@ def _is_dft_failure_results(failure_results: Union[Dict[str, Any], str]) -> bool
                     and "error" in failure_results["results"][0] \
                     and isinstance(failure_results["results"][0]["error"], dict) \
                     and "error_type" in failure_results["results"][0]["error"] \
-                    and "error_message" in failure_results["results"][0]["error"]
+                    and "error_message" in failure_results["results"][0]["error"]
+
+    @classmethod
+    def from_input_data_container(
+        cls,
+        workspace: "Workspace",
+        name: str,
+        target: str,
+        input_data: bytes,
+        batch_input_blobs: Dict[str, bytes],
+        content_type: ContentType = ContentType.json,
+        blob_name: str = "inputData",
+        encoding: str = "",
+        job_id: str = None,
+        container_name: str = None,
+        provider_id: str = None,
+        input_data_format: str = None,
+        output_data_format: str = None,
+        input_params: Dict[str, Any] = None,
+        session_id: Optional[str] = None,
+        **kwargs
+    ) -> "BaseJob":
+        """Create a new Azure Quantum job based on a list of input_data.
+
+        :param workspace: Azure Quantum workspace to submit the input_data to
+        :type workspace: Workspace
+        :param name: Name of the job
+        :type name: str
+        :param target: Azure Quantum target
+        :type target: str
+        :param input_data: Raw input data to submit
+        :type input_data: Dict
+        :param blob_name: Dict of Input data json to gives a table of contents
+        :type batch_input_blobs: Dict
+        :param blob_name: Dict of QcSchema Data where the key is the blob name to store it in the container
+        :type blob_name: str
+        :param content_type: Content type, e.g. "application/json"
+        :type content_type: ContentType
+        :param encoding: input_data encoding, e.g. "gzip", defaults to empty string
+        :type encoding: str
+        :param job_id: Job ID, defaults to None
+        :type job_id: str
+        :param container_name: Container name, defaults to None
+        :type container_name: str
+        :param provider_id: Provider ID, defaults to None
+        :type provider_id: str
+        :param input_data_format: Input data format, defaults to None
+        :type input_data_format: str
+        :param output_data_format: Output data format, defaults to None
+        :type output_data_format: str
+        :param input_params: Input parameters, defaults to None
+        :type input_params: Dict[str, Any]
+        :param input_params: Input params for job
+        :type input_params: Dict[str, Any]
+        :return: Azure Quantum Job
+        :rtype: Job
+        """
+        # Generate job ID if not specified
+        if job_id is None:
+            job_id = cls.create_job_id()
+
+        # Create container if it does not yet exist
+        container_uri = workspace.get_container_uri(
+            job_id=job_id,
+            container_name=container_name
+        )
+        logger.debug(f"Container URI: {container_uri}")
+
+        # Upload Input Data
+        input_data_uri = cls.upload_input_data(
+            container_uri=container_uri,
+            input_data=input_data,
+            content_type=content_type,
+            blob_name=blob_name,
+            encoding=encoding,
+        )
+
+        # Upload data to container
+        for blob_name, input_data_item in batch_input_blobs.items():
+            cls.upload_input_data(
+                container_uri=container_uri,
+                input_data=input_data_item,
+                content_type=content_type,
+                blob_name=blob_name,
+                encoding=encoding,
+            )
+
+        # Create and submit job
+        return cls.from_storage_uri(
+            workspace=workspace,
+            job_id=job_id,
+            target=target,
+            input_data_uri=input_data_uri,
+            container_uri=container_uri,
+            name=name,
+            input_data_format=input_data_format,
+            output_data_format=output_data_format,
+            provider_id=provider_id,
+            input_params=input_params,
+            session_id=session_id,
+            **kwargs
+        )