Local Storage

from mpqp.local_storage import *

The local_storage module is responsible for handling the storage of results and jobs data locally on the user’s device using a SQLite database. The functions defined here are for the most part callable directly on the Result, BatchResult and Job classes.

Functions provided here are decomposed in two broad categories:

the ones used to interact with MPQP objects by the end user, presented in sections Saving, Loading and Deleting,
the ones used to interact more closely with the database, aimed more at internal use, presented in sections The Setup and Querying.

Saving

Provides functions to insert Job and Result into the local database.

insert_jobs(jobs)[source]

Insert a job in the database.

Method corresponding: save().

Parameters: jobs (mpqp.execution.job.Job | list[mpqp.execution.job.Job]) – The job(s) to be inserted.
Returns: The ID of the newly inserted job.
Return type: list[int]

Example

>>> job = Job(JobType.STATE_VECTOR, QCircuit(2), IBMDevice.AER_SIMULATOR)
>>> insert_jobs(job)
[7]

insert_results(result, reuse_similar_job=True)[source]

Insert a result or batch result into the database.

Methods corresponding: Result.save and BatchResult.save.

Parameters

result (mpqp.execution.result.Result | mpqp.execution.result.BatchResult | list[mpqp.execution.result.Result]) – The result(s) to be inserted.
reuse_similar_job (bool) – If True, checks for an existing job in the database and reuses its ID to avoid duplicates.

Returns

List of IDs of the inserted result(s).

Return type

list[int]

Example

>>> result = Result(Job(JobType.STATE_VECTOR, QCircuit(2), IBMDevice.AER_SIMULATOR), StateVector([1, 0, 0, 0]))
>>> insert_results(result)
[8]

Loading

This module provides utility functions retrieving jobs and results from local storage. In the process, they are converted to MPQP objects (Job and Result).

get_all_jobs()[source]

Retrieve all jobs from the local storage and convert them into Job.

Method of the class corresponding: load_all().

Returns: All locally stored jobs.
Return type: list[mpqp.execution.job.Job]

Example

>>> for job in get_all_jobs(): 
...     print(job)
Job(JobType.SAMPLE, QCircuit(...), IBMDevice.AER_SIMULATOR, BasisMeasure(...))
Job(JobType.SAMPLE, QCircuit(...), GOOGLEDevice.CIRQ_LOCAL_SIMULATOR, BasisMeasure(...))
Job(JobType.SAMPLE, QCircuit(...), IBMDevice.AER_SIMULATOR, BasisMeasure(...))
Job(JobType.SAMPLE, QCircuit(...), GOOGLEDevice.CIRQ_LOCAL_SIMULATOR, BasisMeasure(...))
Job(JobType.STATE_VECTOR, QCircuit(...), IBMDevice.AER_SIMULATOR)
Job(JobType.STATE_VECTOR, QCircuit(...), IBMDevice.AER_SIMULATOR)

get_all_results()[source]

Retrieve all results from the local storage and convert them into Result.

Method of the class corresponding: load_all().

Returns: All locally stored results.
Return type: list[mpqp.execution.result.Result]

Example

>>> for result in get_all_results(): 
...     print(repr(result))
Result(Job(JobType.SAMPLE, QCircuit(...), IBMDevice.AER_SIMULATOR, BasisMeasure(...), [Sample(...), Sample(...)], None, 1024)
Result(Job(JobType.SAMPLE, QCircuit(...), IBMDevice.AER_SIMULATOR, BasisMeasure(...), [Sample(...), Sample(...)], None, 1024)
Result(Job(JobType.SAMPLE, QCircuit(...), GOOGLEDevice.CIRQ_LOCAL_SIMULATOR, BasisMeasure(...), [Sample(...), Sample(...)], None, 1024)
Result(Job(JobType.SAMPLE, QCircuit(...), IBMDevice.AER_SIMULATOR, BasisMeasure(...), [Sample(...), Sample(...)], None, 1024)
Result(Job(JobType.SAMPLE, QCircuit(...), GOOGLEDevice.CIRQ_LOCAL_SIMULATOR, BasisMeasure(...), [Sample(...), Sample(...)], None, 1024)
Result(Job(JobType.STATE_VECTOR, QCircuit(...), IBMDevice.AER_SIMULATOR), StateVector(...), 0, 0)
Result(Job(JobType.STATE_VECTOR, QCircuit(...), IBMDevice.AER_SIMULATOR), StateVector(...), 0, 0)

get_jobs_with_id(job_id)[source]

Retrieve jobs with the given ID(s).

Method of the class corresponding: load_by_local_id().

Parameters: job_id (int | list[int]) – ID(s) to search for.
Returns: Job(s) corresponding to the id(s).
Return type: list[mpqp.execution.job.Job]

Example

>>> jobs = get_jobs_with_id([1, 2, 3])
>>> for job in jobs: 
...     print(job)
Job(JobType.SAMPLE, QCircuit(...), IBMDevice.AER_SIMULATOR, BasisMeasure(...))
Job(JobType.SAMPLE, QCircuit(...), GOOGLEDevice.CIRQ_LOCAL_SIMULATOR, BasisMeasure(...))
Job(JobType.SAMPLE, QCircuit(...), IBMDevice.AER_SIMULATOR, BasisMeasure(...))

get_jobs_with_job(job)[source]

Retrieve job(s) matching the given job(s) attributes from the database

job type,
circuit,
device,
measure.

Method of the class corresponding: load_similar().

Parameters: job (mpqp.execution.job.Job | list[mpqp.execution.job.Job]) – Job(s) to search for.
Returns: Matching job(s) corresponding to the job(s) attributes
Return type: list[mpqp.execution.job.Job]

Example

>>> job = Job(JobType.STATE_VECTOR, QCircuit([], nb_qubits=2, label="circuit 1"), IBMDevice.AER_SIMULATOR)
>>> print(get_jobs_with_job(job)) 
[Job(JobType.STATE_VECTOR, QCircuit(...), IBMDevice.AER_SIMULATOR)]

get_jobs_with_result(result)[source]

Retrieve job(s) associated with the given result(s) attributes from the database:

data,
error,
shots.

Parameters: result (mpqp.execution.result.Result | list[mpqp.execution.result.Result] | mpqp.execution.result.BatchResult) – Result(s) to find associated jobs for.
Returns: Matching job(s) corresponding to the result(s) attribute and job attribute of the result(s).
Return type: list[mpqp.execution.job.Job]

Example

>>> result = Result(
...     Job(JobType.STATE_VECTOR, QCircuit([], nb_qubits=2, label="circuit 1"), IBMDevice.AER_SIMULATOR),
...     StateVector([1, 0, 0, 0]),
...     0,
...     0,
... )
>>> print(get_jobs_with_result(result)) 
[Job(JobType.STATE_VECTOR, QCircuit(...), IBMDevice.AER_SIMULATOR)]

get_results_with_id(result_id)[source]

Retrieve results with the given ID(s).

Method of the class corresponding: load_by_local_id().

Parameters: result_id (int | list[int]) – ID(s) to search for.
Returns: Matching result(s) corresponding to the id(s).
Return type: list[mpqp.execution.result.Result]

Example

>>> for result in get_results_with_id(1): 
...     print(repr(result))
Result(Job(JobType.SAMPLE, QCircuit(...), IBMDevice.AER_SIMULATOR, BasisMeasure(...), [Sample(...), Sample(...)], None, 1024)
>>> for result in get_results_with_id([2, 3]): 
...     print(repr(result))
Result(Job(JobType.SAMPLE, QCircuit(...), IBMDevice.AER_SIMULATOR, BasisMeasure(...), [Sample(...), Sample(...)], None, 1024)
Result(Job(JobType.SAMPLE, QCircuit(...), GOOGLEDevice.CIRQ_LOCAL_SIMULATOR, BasisMeasure(...), [Sample(...), Sample(...)], None, 1024)

get_results_with_job_id(job_id)[source]

Retrieve results associated with the given job ID(s).

Method of the class corresponding: load_by_local_job_id().

Parameters: job_id (int | list[int]) – ID(s) to search for.
Returns: Results corresponding to the job id(s).
Return type: list[mpqp.execution.result.Result]

Example

>>> results = get_results_with_job_id(1)
>>> for result in results: 
...     print(repr(result))
Result(Job(JobType.SAMPLE, QCircuit(...), IBMDevice.AER_SIMULATOR, BasisMeasure(...), [Sample(...), Sample(...)], None, 1024)
Result(Job(JobType.SAMPLE, QCircuit(...), IBMDevice.AER_SIMULATOR, BasisMeasure(...), [Sample(...), Sample(...)], None, 1024)

get_results_with_result(result)[source]

Retrieve result(s) matching specific result(s) attributes:

data,
error,
shots.

Method of the class corresponding: load_similar().

Parameters: result (mpqp.execution.result.Result | list[mpqp.execution.result.Result] | mpqp.execution.result.BatchResult) – Result(s) to search for.
Returns: Matching result(s) corresponding to the result(s) attribute.
Return type: list[mpqp.execution.result.Result]

Example

>>> result = Result(
...     Job(JobType.STATE_VECTOR, QCircuit([], nb_qubits=2, label="circuit 1"), IBMDevice.AER_SIMULATOR),
...     StateVector([1, 0, 0, 0]),
...     0,
...     0,
... )
>>> for result in get_results_with_result(result): 
...     print(repr(result))
Result(Job(JobType.STATE_VECTOR, QCircuit(...), IBMDevice.AER_SIMULATOR), StateVector(...), 0, 0)
Result(Job(JobType.STATE_VECTOR, QCircuit(...), IBMDevice.AER_SIMULATOR), StateVector(...), 0, 0)

get_results_with_result_and_job(result)[source]

Retrieve result(s) associated with specific result(s) attributes:

data,
error,
shots.

And also with the job attribute of the result(s):

job type,
circuit,
device,
measure.

Parameters: result (mpqp.execution.result.Result | list[mpqp.execution.result.Result] | mpqp.execution.result.BatchResult) – Result(s) to search for.
Returns: Matching result(s) corresponding to the result(s) attribute and job attribute of the result(s).
Return type: list[mpqp.execution.result.Result]

Example

>>> result = Result(
...     Job(JobType.STATE_VECTOR, QCircuit([], nb_qubits=2, label="circuit 1"), IBMDevice.AER_SIMULATOR),
...     StateVector([1, 0, 0, 0]),
...     0,
...     0,
... )
>>> for result in get_results_with_result_and_job(result): 
...     print(repr(result))
Result(Job(JobType.STATE_VECTOR, QCircuit(...), IBMDevice.AER_SIMULATOR), StateVector(...), 0, 0)

jobs_local_storage_to_mpqp(jobs)[source]

Convert a dictionary or list of dictionaries representing jobs into MPQP Job objects.

Parameters: jobs (Optional[Union[list[dict[str, Any]], dict[str, Any]]]) – A dictionary or list of dictionaries retrieved from the database.
Returns: A list of MPQP Job objects.
Return type: list[mpqp.execution.job.Job]

Example

>>> job_local_storage = fetch_jobs_with_id(1)
>>> jobs_local_storage_to_mpqp(job_local_storage) 
[Job(JobType.SAMPLE, QCircuit(...), IBMDevice.AER_SIMULATOR, BasisMeasure(...))]

results_local_storage_to_mpqp(results)[source]

Convert a dictionary or list of dictionaries representing results into a Result.

Parameters: results (Optional[Union[list[dict[str, Any]], dict[str, Any]]]) – The results retrieved from the database.
Returns: The converted result(s).
Return type: list[mpqp.execution.result.Result]

Example

>>> result_local_storage = fetch_results_with_id([1, 2])
>>> results = results_local_storage_to_mpqp(result_local_storage)
>>> for result in results:  
...     print(repr(result))
Result(Job(JobType.SAMPLE, QCircuit(...), IBMDevice.AER_SIMULATOR, BasisMeasure(...), [Sample(2, index=0, count=..., probability=0...), ...], None, 1024)
Result(Job(JobType.SAMPLE, QCircuit(...), IBMDevice.AER_SIMULATOR, BasisMeasure(...), [Sample(2, index=0, count=..., probability=0...), ...], None, 1024)

Deleting

clear_local_storage()[source]

Clears all records from the database, including jobs and results.

This function resets the tables and their auto-increment counters.

Example

>>> clear_local_storage()
>>> fetch_all_results()
[]
>>> fetch_all_jobs()
[]

remove_all_with_job_id(job_id)[source]

Removes jobs and their associated results for the specified job IDs.

Parameters: job_id (int | list[int]) – Job ID(s) to remove.

Example

>>> remove_all_with_job_id(1)
>>> fetch_jobs_with_id(1)
[]
>>> fetch_results_with_job_id(1)
[]
>>> remove_all_with_job_id([2, 3])
>>> fetch_jobs_with_id([2, 3])
[]
>>> fetch_results_with_job_id([2, 3])
[]

remove_jobs_with_id(job_id)[source]

Removes jobs with the specified job IDs.

Method of the class corresponding: delete_by_local_id().

Parameters: job_id (int | list[int]) – Job ID(s) to remove.

Example

>>> remove_jobs_with_id(1)
>>> fetch_jobs_with_id(1)
[]
>>> remove_jobs_with_id([2, 3])
>>> fetch_jobs_with_id([2,3])
[]

remove_jobs_with_jobs_local_storage(jobs)[source]

Removes the matching jobs.

Parameters: jobs (Optional[Union[list[dict[str, Any]], dict[str, Any]]]) – Job dictionary(ies) for which the matching database row should be deleted.

Example

>>> jobs = fetch_jobs_with_id(1)
>>> remove_jobs_with_jobs_local_storage(jobs)
>>> fetch_jobs_with_id(1)
[]

remove_results_with_id(result_id)[source]

Removes results with the specified result IDs.

Method of the class corresponding: delete_by_local_id().

Parameters: result_id (int | list[int]) – Result ID(s) to remove.

Example

>>> remove_results_with_id(1)
>>> fetch_results_with_id(1)
[]
>>> remove_results_with_id([2, 3])
>>> fetch_results_with_id([2, 3])
[]

remove_results_with_job(jobs)[source]

Removes results associated with the specified job(s).

Parameters: jobs (mpqp.execution.job.Job | list[mpqp.execution.job.Job]) – Job(s) to remove results for.

Example

>>> job = Job(JobType.STATE_VECTOR, QCircuit(2), IBMDevice.AER_SIMULATOR)
>>> remove_results_with_job(job)
>>> fetch_results_with_job(job)
[]

remove_results_with_job_id(job_id)[source]

Removes results related to the job(s) who’s ID is given as input.

Parameters: job_id (int | list[int]) – Result Job_ID(s) to remove.

Example

>>> remove_results_with_job_id(1)
>>> fetch_results_with_job_id(1)
[]
>>> remove_results_with_job_id([2, 3])
>>> fetch_results_with_job_id([2, 3])
[]

remove_results_with_result(result)[source]

Removes results matching the given result(s).

Parameters: result (mpqp.execution.result.Result | mpqp.execution.result.BatchResult | list[mpqp.execution.result.Result]) – Result(s) to remove.

Example

>>> result = Result(Job(JobType.STATE_VECTOR, QCircuit(2), IBMDevice.AER_SIMULATOR), StateVector([1, 0, 0, 0]))
>>> remove_results_with_result(result)
>>> fetch_results_with_result(result)
[]

remove_results_with_results_local_storage(results)[source]

Removes the matching results.

Parameters: results (Optional[Union[list[dict[str, Any]], dict[str, Any]]]) – Result dictionary(ies) for which the matching database row should be deleted.

Example

>>> results = fetch_results_with_id(1)
>>> remove_results_with_results_local_storage(results)
>>> fetch_results_with_id(1)
[]

The Setup

This module provides utilities for managing a SQLite database for quantum job and result records, as well as functions for removing entries based on various criteria.

It allows storing and managing job and result metadata related to quantum circuit executions.

ensure_local_storage(func)[source]

Decorator for functions needing the database to be present.

Parameters: func (Callable[[...], T]) – The function to be decorated.
Return type: Callable[[…], T]

get_database_version()[source]

Retrieves the current database version from the version table.

Return type: str

setup_local_storage(path=None)[source]

Sets up a SQLite database for storing quantum job and result records.

Two tables will be created, one for jobs and the other for results.

Parameters: path (Optional[str]) – Directory to save the database file. Defaults to the current working directory.

Example

>>> setup_local_storage("~/documents/my_database.db")
>>> os.remove(Path("~/documents/my_database.db").expanduser())
>>> setup_local_storage("my_database.db")
>>> os.remove("my_database.db")
>>> setup_local_storage()

Querying

provides utility functions to query and fetch data from the quantum job and result database. It includes methods to retrieve all records, specific records by ID, and filtered records based on Job or Result objects.

fetch_all_jobs()[source]

Fetch all job records from the database.

Returns: All jobs as dictionaries.
Return type: list[dict[str, Any]]

Examples

>>> jobs = fetch_all_jobs()
>>> for job in jobs:
...    print("job:", job) 
job: {'id': 1, 'type': 'SAMPLE', 'circuit': '"QCircuit(...)"', 'device': 'IBMDevice.AER_SIMULATOR', 'measure': '"BasisMeasure([0, 1], c_targets=[0, 1])"', 'remote_id': None, 'status': None, 'created_at': '...'}
job: {'id': 2, 'type': 'SAMPLE', 'circuit': '"QCircuit(...)"', 'device': 'GOOGLEDevice.CIRQ_LOCAL_SIMULATOR', 'measure': '"BasisMeasure([0, 1], c_targets=[0, 1])"', 'remote_id': None, 'status': None, 'created_at': '...'}
job: {'id': 3, 'type': 'SAMPLE', 'circuit': '"QCircuit(...)"', 'device': 'IBMDevice.AER_SIMULATOR', 'measure': '"BasisMeasure([0], c_targets=[0])"', 'remote_id': None, 'status': None, 'created_at': '...'}
job: {'id': 4, 'type': 'SAMPLE', 'circuit': '"QCircuit(...)"', 'device': 'GOOGLEDevice.CIRQ_LOCAL_SIMULATOR', 'measure': '"BasisMeasure([0], c_targets=[0])"', 'remote_id': None, 'status': None, 'created_at': '...'}
job: {'id': 5, 'type': 'STATE_VECTOR', 'circuit': '"QCircuit(...)"', 'device': 'IBMDevice.AER_SIMULATOR', 'measure': None, 'remote_id': None, 'status': None, 'created_at': '...'}
job: {'id': 6, 'type': 'STATE_VECTOR', 'circuit': '"QCircuit(...)"', 'device': 'IBMDevice.AER_SIMULATOR', 'measure': None, 'remote_id': None, 'status': None, 'created_at': '...'}

fetch_all_results()[source]

Fetch all result records from the database.

Returns: All results as dictionaries.
Return type: list[dict[str, Any]]

Examples

>>> results = fetch_all_results()
>>> for result in results:
...    print("result:", result) 
result: {'id': 1, 'job_id': 1, 'data': '"[Sample(...), Sample(...)]"', 'error': None, 'shots': 1024, 'created_at': '...'}
result: {'id': 2, 'job_id': 1, 'data': '"[Sample(...), Sample(...)]"', 'error': None, 'shots': 1024, 'created_at': '...'}
result: {'id': 3, 'job_id': 2, 'data': '"[Sample(...), Sample(...)]"', 'error': None, 'shots': 1024, 'created_at': '...'}
result: {'id': 4, 'job_id': 3, 'data': '"[Sample(...), Sample(...)]"', 'error': None, 'shots': 1024, 'created_at': '...'}
result: {'id': 5, 'job_id': 4, 'data': '"[Sample(...), Sample(...)]"', 'error': None, 'shots': 1024, 'created_at': '...'}
result: {'id': 6, 'job_id': 5, 'data': '"StateVector([1, 0, 0, 0])"', 'error': '"0"', 'shots': 0, 'created_at': '...'}
result: {'id': 7, 'job_id': 6, 'data': '"StateVector([1, 0, 0, 0])"', 'error': '"0"', 'shots': 0, 'created_at': '...'}

fetch_jobs_with_id(job_id)[source]

Fetch job(s) by their ID(s).

Parameters: job_id (int | list[int]) – The ID(s) of the job(s) to fetch.
Returns: Matching job(s) as dictionaries corresponding to the id(s).
Return type: list[dict[str, Any]]

Examples

>>> jobs = fetch_jobs_with_id(1)
>>> for job in jobs:
...    print("job_id:", job['id'])
job_id: 1
>>> jobs = fetch_jobs_with_id([2, 3])
>>> for job in jobs:
...    print("job_id:", job['id'])
job_id: 2
job_id: 3

fetch_jobs_with_job(job)[source]

Fetch job(s) records matching specific job(s) attributes:

job type,
circuit,
device,
measure.

Parameters: job (mpqp.execution.job.Job | list[mpqp.execution.job.Job]) – Job(s) to match.
Returns: Matching job(s) as dictionaries corresponding to the job(s) attributes
Return type: list[dict[str, Any]]

Examples

>>> job = Job(JobType.STATE_VECTOR, QCircuit([], nb_qubits=2, label="circuit 1"), IBMDevice.AER_SIMULATOR)
>>> jobs = fetch_jobs_with_job(job)
>>> for job in jobs:
...    print("job:", job) 
job: {'id': 5, 'type': 'STATE_VECTOR', 'circuit': '"QCircuit(...)"', 'device': 'IBMDevice.AER_SIMULATOR', 'measure': None, 'remote_id': None, 'status': None, 'created_at': '...'}

fetch_jobs_with_result(result)[source]

Fetch job(s) associated with specific results(s) attributes:

data,

error,

shots.

Parameters: result (mpqp.execution.result.Result | mpqp.execution.result.BatchResult | list[mpqp.execution.result.Result]) – Result(s) to match.
Returns: Matching job(s) as dictionaries corresponding to the result(s) attribute.
Return type: list[dict[str, Any]]

Examples

>>> result = Result(Job(JobType.STATE_VECTOR, QCircuit([], nb_qubits=2, label="circuit 1"), IBMDevice.AER_SIMULATOR,), StateVector([1, 0, 0, 0]),0,0)
>>> jobs = fetch_jobs_with_result(result)
>>> for job in jobs:
...    print("job:", job) 
job: {'id': 5, 'type': 'STATE_VECTOR', 'circuit': '"QCircuit(...)"', 'device': 'IBMDevice.AER_SIMULATOR', 'measure': None, 'remote_id': None, 'status': None, 'created_at': '...'}
job: {'id': 6, 'type': 'STATE_VECTOR', 'circuit': '"QCircuit(...)"', 'device': 'IBMDevice.AER_SIMULATOR', 'measure': None, 'remote_id': None, 'status': None, 'created_at': '...'}

fetch_jobs_with_result_and_job(result)[source]

Fetch job(s) associated with specific results(s) attributes:

data,
error,
shots.

And also with the job attribute of the results(s):

job type,
circuit,
device,
measure.

Parameters: result (mpqp.execution.result.Result | mpqp.execution.result.BatchResult | list[mpqp.execution.result.Result]) – Result(s) to match.
Returns: Matching job(s) as dictionaries corresponding to the result(s) attribute and job attribute of the result(s).
Return type: list[dict[str, Any]]

Examples

>>> result = Result(Job(JobType.STATE_VECTOR, QCircuit([], nb_qubits=2, label="circuit 1"), IBMDevice.AER_SIMULATOR,), StateVector([1, 0, 0, 0]),0,0)
>>> jobs = fetch_jobs_with_result_and_job(result)
>>> for job in jobs:
...    print("job:", job) 
job: {'id': 5, 'type': 'STATE_VECTOR', 'circuit': '"QCircuit(...)"', 'device': 'IBMDevice.AER_SIMULATOR', 'measure': None, 'remote_id': None, 'status': None, 'created_at': '...'}

fetch_results_with_id(result_id)[source]

Fetch result(s) by their ID(s).

Parameters: result_id (int | list[int]) – The ID(s) of the result(s) to fetch.
Returns: Matching result(s) as dictionaries corresponding to the id(s).
Return type: list[dict[str, Any]]

Examples

>>> results = fetch_results_with_id([1, 2, 3])
>>> for result in results:
...    print("result:", result) 
result: {'id': 1, 'job_id': 1, 'data': '"[Sample(...), Sample(...)]"', 'error': None, 'shots': 1024, 'created_at': '...'}
result: {'id': 2, 'job_id': 1, 'data': '"[Sample(...), Sample(...)]"', 'error': None, 'shots': 1024, 'created_at': '...'}
result: {'id': 3, 'job_id': 2, 'data': '"[Sample(...), Sample(...)]"', 'error': None, 'shots': 1024, 'created_at': '...'}

fetch_results_with_job(jobs)[source]

Fetch result(s) associated with the job attribute of the results(s)

job type,
circuit,
device,
measure.

Parameters: jobs (mpqp.execution.job.Job | list[mpqp.execution.job.Job]) – The job(s) to match.
Returns: Matching result(s) as dictionaries corresponding to job attribute of the result(s).
Return type: list[dict[str, Any]]

Examples

>>> job = Job(JobType.STATE_VECTOR, QCircuit([], nb_qubits=2, label="circuit 1"), IBMDevice.AER_SIMULATOR)
>>> results = fetch_results_with_job(job)
>>> for result in results:
...    print("result:", result) 
result: {'id': 6, 'job_id': 5, 'data': '"StateVector([1, 0, 0, 0])"', 'error': '"0"', 'shots': 0, 'created_at': '...'}

fetch_results_with_job_id(job_id)[source]

Fetch result(s) associated with specific job ID(s).

Parameters: job_id (int | list[int]) – The ID(s) of job(s) to which the desired result(s) are attached.
Returns: Matching result(s) as dictionaries corresponding to the job id(s).
Return type: list[dict[str, Any]]

Examples

>>> results = fetch_results_with_job_id(1)
>>> for result in results:
...    print("result_id:", result['id'], ", job_id:", result['job_id'])
result_id: 1 , job_id: 1
result_id: 2 , job_id: 1

fetch_results_with_result(result)[source]

Fetch result(s) matching specific results(s) attributes:

data,
error,
shots.

Parameters: result (mpqp.execution.result.Result | mpqp.execution.result.BatchResult | list[mpqp.execution.result.Result]) – The result(s) to match.
Returns: Matching result(s) as dictionaries corresponding to the result(s) attribute.
Return type: list[dict[str, Any]]

Examples

>>> result = Result(Job(JobType.STATE_VECTOR, QCircuit([], nb_qubits=2, label="circuit 1"), IBMDevice.AER_SIMULATOR), StateVector([1, 0, 0, 0]), 0, 0)
>>> results = fetch_results_with_result(result)
>>> for result in results:
...    print("result:", result) 
result: {'id': 6, 'job_id': 5, 'data': '"StateVector([1, 0, 0, 0])"', 'error': '"0"', 'shots': 0, 'created_at': '...'}
result: {'id': 7, 'job_id': 6, 'data': '"StateVector([1, 0, 0, 0])"', 'error': '"0"', 'shots': 0, 'created_at': '...'}

fetch_results_with_result_and_job(result)[source]

Fetch result(s) associated with specific results(s) attributes:

data,
error,
shots.

And also with the job attribute of the results(s):

job type,
circuit,
device,
measure.

Parameters: result (mpqp.execution.result.Result | mpqp.execution.result.BatchResult | list[mpqp.execution.result.Result]) – The Result(s) to match.
Returns: Matching result(s) as dictionaries corresponding to the result(s) attribute and job attribute of the result(s).
Return type: list[dict[str, Any]]

Examples

>>> result = Result(Job(JobType.STATE_VECTOR, QCircuit([], nb_qubits=2, label="circuit 1"), IBMDevice.AER_SIMULATOR), StateVector([1, 0, 0, 0]), 0, 0)
>>> results = fetch_results_with_result_and_job(result)
>>> for result in results:
...    print("result:", result) 
result: {'id': 6, 'job_id': 5, 'data': '"StateVector([1, 0, 0, 0])"', 'error': '"0"', 'shots': 0, 'created_at': '...'}