`pyqpanda.OriginService`

QPanda Python

Copyright (C) Origin Quantum 2017-2020

Licensed Under Apache Licence 2.0

Submodules

Package Contents

Classes

`QCloud`	Quantum Computing Cloud Service Utility Class.
`QPilotOSMachine`	This class can submit Quantum Program to PilotOS.

class pyqpanda.OriginService.QCloud[源代码]

Bases: pyqpanda.QCloudService

Quantum Computing Cloud Service Utility Class.

The primary function of this utility class for the Origin Quantum Computing Cloud Service is to package and send quantum circuits to the remote computing service (Quantum Cloud). It then queries the computation results through polling, supporting various simulators and real hardware.

User API key authentication is required for computations. Please obtain it here : http://qcloud.originqc.com.cn/

class CloudQMchineType

Bases: Enum

Generic enumeration.

Derive from this class to define new enumerations.

Full_AMPLITUDE = 0

NOISE_QMACHINE = 1

PARTIAL_AMPLITUDE = 2

SINGLE_AMPLITUDE = 3

CHEMISTRY = 4

REAL_CHIP = 5

QST = 6

FIDELITY = 7

class TaskStatus

Bases: Enum

Generic enumeration.

Derive from this class to define new enumerations.

WAITING = 1

COMPUTING = 2

FINISHED = 3

FAILED = 4

QUEUING = 5

SENT_TO_BUILD_SYSTEM = 6

BUILD_SYSTEM_ERROR = 7

SEQUENCE_TOO_LONG = 8

BUILD_SYSTEM_RUN = 9.0

pqc_init()[源代码]

init_qvm(token: str, is_logged: bool = False, use_bin_or_hex=True, enable_pqc_encryption=False, random_num: bytes | str = os.urandom(96), request_time_out=100)[源代码]: init quantum virtual machine

convert_result_format(input_dict: dict, binary_size: int)[源代码]

convert result format from binary to hex

Args:: input_dict (dict): origin result dict use_bin_or_hex (bool): True -> use binary result format, False -> Hex
Returns:: result: dict.

query_task_state_result(task_id: str)[源代码]

query_batch_task_state_result(task_id: str)[源代码]

estimate_price(qubit_num: int, shot: int, qprogCount: int = 1, epoch: int = 1)[源代码]

async_full_amplitude_measure(prog: pyqpanda.QProg, shot: int, task_name: str = 'QPanda Experiment')[源代码]

Execute a full amplitude measurement on the Quantum Cloud Service.

Args:: prog (QProg): Quantum program containing the circuit to be measured. shot (int): Number of measurements to perform. task_name (str, optional): Task name for identification. Defaults to 'QPanda Experiment'.
Returns:: Task_id[str]: A task id for current task

full_amplitude_measure(prog: pyqpanda.QProg, shot: int, task_name: str = 'QPanda Experiment')[源代码]

Execute a full amplitude measurement on the Quantum Cloud Service.

Args:: prog (QProg): Quantum program containing the circuit to be measured. shot (int): Number of measurements to perform. task_name (str, optional): Task name for identification. Defaults to 'QPanda Experiment'.
Returns:: Dict[str, float]: Dictionary containing probabilities of measurement outcomes.

async_full_amplitude_pmeasure(prog: pyqpanda.QProg, qvec: List[int], task_name: str = 'QPanda Experiment')[源代码]

Execute a full amplitude probability measurement on the Quantum Cloud Service.

Args:: prog (QProg): Quantum program containing the circuit to be measured. shot (int): Number of measurements to perform. task_name (str, optional): Task name for identification. Defaults to 'QPanda Experiment'.
Returns:: Task_id[str]: A task id for current task

full_amplitude_pmeasure(prog: pyqpanda.QProg, qvec: List[int], task_name: str = 'QPanda Experiment')[源代码]

Execute a full amplitude probability measurement on the Quantum Cloud Service.

Args:: prog (QProg): Quantum program containing the circuit to be measured. qvec (List[int]): List of qubits to be measured. task_name (str, optional): Task name for identification. Defaults to 'QPanda Experiment'.
Returns:: Dict[str, float]: Dictionary containing probabilities of measurement outcomes.

get_expectation(prog: pyqpanda.QProg, hamiltonian: List[Tuple[Dict[int, str], float]], qvec: pyqpanda.QVec, task_name: str = 'QPanda Experiment')[源代码]

Calculate the expectation value of a Hamiltonian on the Quantum Cloud Service.

Args:: prog (QProg): Quantum program containing the circuit for state preparation. hamiltonian (List[Tuple[Dict[int, str], float]]): List of terms in the Hamiltonian along with their coefficients. qvec (QVec): List of qubits representing the quantum state. task_name (str, optional): Task name for identification. Defaults to 'QPanda Experiment'.
Returns:: float: Expectation value of the Hamiltonian.

get_state_fidelity(prog: pyqpanda.QProg, shot: int, chip_id: int = 2, is_amend: bool = True, is_mapping: bool = True, is_optimization: bool = True, task_name: str = 'QPanda Experiment')[源代码]

Get the state fidelity of the Quantum Real Chip.

Args:: prog (QProg): Quantum program containing the circuit for state preparation. shot (int): Number of measurements to perform. chip_id (int, optional): ID of the quantum chip. Defaults to 2. is_amend (bool, optional): Flag for amplitude amplification. Defaults to True. is_mapping (bool, optional): Flag for qubit mapping. Defaults to True. is_optimization (bool, optional): Flag for gate optimization. Defaults to True. task_name (str, optional): Task name for identification. Defaults to 'QPanda Experiment'.
Returns:: float: State fidelity value.

get_state_tomography_density(prog: pyqpanda.QProg, shot: int, chip_id: int = 2, is_amend: bool = True, is_mapping: bool = True, is_optimization: bool = True, task_name: str = 'QPanda Experiment')[源代码]

Get the density matrix for state tomography on the Quantum Cloud Service.

Args:: prog (QProg): Quantum program containing the circuit for state preparation. shot (int): Number of measurements to perform. chip_id (int, optional): ID of the quantum chip. Defaults to 2. is_amend (bool, optional): Flag for amplitude amplification. Defaults to True. is_mapping (bool, optional): Flag for qubit mapping. Defaults to True. is_optimization (bool, optional): Flag for gate optimization. Defaults to True. task_name (str, optional): Task name for identification. Defaults to 'QPanda Experiment'.
Returns:: List[List[complex]]: Density matrix representing the quantum state.

async_noise_measure(prog: pyqpanda.QProg, shot: int, task_name: str = 'QPanda Experiment')[源代码]

Measure noise in the quantum computation.

Args:: prog (QProg): The quantum circuit to be executed. shot (int): The number of shots (measurement repetitions). task_name (str, optional): The name of the QPanda Experiment task. Defaults to 'QPanda Experiment'.
Returns:: Task_id[str]: A task id for current task

noise_measure(prog: pyqpanda.QProg, shot: int, task_name: str = 'QPanda Experiment')[源代码]

Measure noise in the quantum computation.

Args:: prog (QProg): The quantum circuit to be executed. shot (int): The number of shots (measurement repetitions). task_name (str, optional): The name of the QPanda Experiment task. Defaults to 'QPanda Experiment'.
Returns:: Dict[str, float]: A dictionary containing measurement results.

async_partial_amplitude_pmeasure(prog: pyqpanda.QProg, amp_vec: List[str], task_name: str = 'QPanda Experiment')[源代码]

Perform partial amplitude measurement in the quantum computation.

Args:: prog (QProg): The quantum circuit to be executed. amp_vec (List[str]): List of amplitude vectors to be measured. task_name (str, optional): The name of the QPanda Experiment task. Defaults to 'QPanda Experiment'.
Returns:: Task_id[str]: A task id for current task

partial_amplitude_pmeasure(prog: pyqpanda.QProg, amp_vec: List[str], task_name: str = 'QPanda Experiment')[源代码]

Perform partial amplitude measurement in the quantum computation.

Args:: prog (QProg): The quantum circuit to be executed. amp_vec (List[str]): List of amplitude vectors to be measured. task_name (str, optional): The name of the QPanda Experiment task. Defaults to 'QPanda Experiment'.
Returns:: Dict[str, complex]: A dictionary containing complex-valued measurement results.

async_real_chip_measure(prog: pyqpanda.QProg | str, shot: int, chip_id: int = 2, is_amend: bool = True, is_mapping: bool = True, is_optimization: bool = True, task_name: str = 'QPanda Experiment', task_from=4)[源代码]

Perform measurements on a real quantum chip.

Args:: prog (QProg): The quantum circuit to be executed. shot (int): The number of shots (measurement repetitions). chip_id (int, optional): Identifier for the specific quantum chip. Defaults to 2. is_amend (bool, optional): Flag indicating whether to perform amendments. Defaults to True. is_mapping (bool, optional): Flag indicating whether to perform qubit mapping. Defaults to True. is_optimization (bool, optional): Flag indicating whether to perform optimization. Defaults to True. task_name (str, optional): The name of the QPanda Experiment task. Defaults to 'QPanda Experiment'. task_from (int): Task source identifier (default is 4, represented by QPanda/pyqpanda).
Returns:: taskid[str]: real chip task id

pqc_encrypt(data: str)[源代码]

Encrypts the given data using the pqc encryption algorithm.

Args:: data (str): The data to be encrypted.
Returns:: str: The encrypted data.

pqc_encrypt_and_combine_json(json_str: str)[源代码]

Encrypts the 'code' field and the remaining JSON data, and combines them into a new JSON string.

Args:: json_str (str): The original JSON string.
Returns:: str: The new JSON string with encrypted 'code' field and remaining JSON data.

pqc_encrypt_and_combine_batch_json(json_str: str)[源代码]

Encrypts the 'code' field and the remaining JSON data, and combines them into a new JSON string.

Args:: json_str (str): The original JSON string.
Returns:: str: The new JSON string with encrypted 'code' field and remaining JSON data.

real_chip_measure(prog: pyqpanda.QProg | str, shot: int, chip_id: int = 2, is_amend: bool = True, is_mapping: bool = True, is_optimization: bool = True, task_name: str = 'QPanda Experiment', task_from=4)[源代码]

Perform measurements on a real quantum chip.

Args:: prog (QProg): The quantum circuit to be executed. shot (int): The number of shots (measurement repetitions). chip_id (int, optional): Identifier for the specific quantum chip. Defaults to 2. is_amend (bool, optional): Flag indicating whether to perform amendments. Defaults to True. is_mapping (bool, optional): Flag indicating whether to perform qubit mapping. Defaults to True. is_optimization (bool, optional): Flag indicating whether to perform optimization. Defaults to True. task_name (str, optional): The name of the QPanda Experiment task. Defaults to 'QPanda Experiment'. task_from (int): Task source identifier (default is 4, represented by QPanda/pyqpanda).
Returns:: Dict[str, float]: A dictionary containing measurement results.

async_batch_real_chip_measure(prog_array: List[pyqpanda.QProg] | List[str], shot: int, chip_id: pyqpanda.real_chip_type = real_chip_type.origin_72, is_amend: bool = True, is_mapping: bool = True, is_optimization: bool = True, batch_id: str = '', task_from=4)[源代码]

Measure a batch of quantum programs on a real quantum chip.

Parameters: - prog_array (List[QProg]): List of quantum programs to be executed. - shot (int): Number of shots (measurements) to perform for each program. - chip_id (real_chip_type, optional): ID of the real quantum chip to use (default is real_chip_type.origin_72). - is_amend (bool, optional): Whether to perform amendment on the programs (default is True). - is_mapping (bool, optional): Whether to perform qubit mapping (default is True). - is_optimization (bool, optional): Whether to perform gate fusion optimization (default is True). - task_from (int): Task source identifier (default is 4, represented by QPanda/pyqpanda). - batch_id (str): The current batch number information for batch tasks, default to empty

Returns: batch_task_id[str]: batch task id

Note: The function submits a batch of quantum programs for execution on a real quantum chip, retrieves the results, and returns the probabilities of measurement outcomes for each program.

batch_real_chip_measure(prog_array: List[pyqpanda.QProg] | List[str], shot: int, chip_id: pyqpanda.real_chip_type = real_chip_type.origin_72, is_amend: bool = True, is_mapping: bool = True, is_optimization: bool = True, batch_id: str = '', task_from=4)[源代码]

Measure a batch of quantum programs on a real quantum chip.

Parameters: - prog_array (List[QProg]): List of quantum programs to be executed. - shot (int): Number of shots (measurements) to perform for each program. - chip_id (real_chip_type, optional): ID of the real quantum chip to use (default is real_chip_type.origin_72). - is_amend (bool, optional): Whether to perform amendment on the programs (default is True). - is_mapping (bool, optional): Whether to perform qubit mapping (default is True). - is_optimization (bool, optional): Whether to perform gate fusion optimization (default is True). - task_from (int): Task source identifier (default is 4, represented by QPanda/pyqpanda). - batch_id (str): The current batch number information for batch tasks, default to empty

Returns: List[Dict[str, float]]: A list of dictionaries containing the probabilities of measurement outcomes for each program.

Note: The function submits a batch of quantum programs for execution on a real quantum chip, retrieves the results, and returns the probabilities of measurement outcomes for each program.

set_noise_model(model: pyqpanda.NoiseModel, single_gate_params: List[float], single_param_list: List[float])[源代码]

Set the noise model for quantum computation.

Parameters: - model (NoiseModel): The noise model to be set. - single_gate_params (List[float]): List of parameters for single-qubit gates. - single_param_list (List[float]): List of parameters for single-qubit gates.

Returns: None

set_qcloud_url(prefix_url: str)[源代码]

Set the QCloud API endpoint.

Parameters: - prefix_url (str): The prefix URL of the QCloud API.

Returns: None

async_single_amplitude_pmeasure(prog: pyqpanda.QProg, amplitude: str, task_name: str = 'QPanda Experiment')[源代码]

Measure the single amplitude of a quantum state.

Parameters: - prog (QProg): The quantum program containing the state. - amplitude (str): The amplitude to measure. - task_name (str): Name of the QPanda Experiment task.

Returns:: Task_id[str]: A task id for current task

single_amplitude_pmeasure(prog: pyqpanda.QProg, amplitude: str, task_name: str = 'QPanda Experiment')[源代码]

Measure the single amplitude of a quantum state.

Parameters: - prog (QProg): The quantum program containing the state. - amplitude (str): The amplitude to measure. - task_name (str): Name of the QPanda Experiment task.

Returns: complex: The measured amplitude.

pec_error_mitigation(prog: pyqpanda.QProg, shot: int, expectations: List[str], chip_id: int = 72, task_name: str = 'QPanda Experiment')[源代码]

Apply PEC error mitigation to correct measurement errors.

Parameters: - prog (QProg): The quantum program to apply error mitigation. - shot (int): Number of shots for measurements. - expectations (List[str]): List of measurement expectations. - chip_id (int): Chip identifier. - task_name (str): Name of the QPanda Experiment task.

Returns: List[float]: List of corrected expectation values.

read_out_error_mitigation(prog: pyqpanda.QProg, shot: int, expectations: List[str], chip_id: int = 72, task_name: str = 'QPanda Experiment')[源代码]

Apply readout error mitigation to correct measurement errors.

Parameters: - prog (QProg): The quantum program to apply error mitigation. - shot (int): Number of shots for measurements. - expectations (List[str]): List of measurement expectations. - chip_id (int): Chip identifier. - task_name (str): Name of the QPanda Experiment task.

Returns:: Dict[str, float]: Dictionary of corrected expectation values.

zne_error_mitigation(prog: pyqpanda.QProg, shot: int, expectations: List[str], noise_strength: List[float], chip_id: int = 72, task_name: str = 'QPanda Experiment')[源代码]

Apply zero-noise extrapolation (ZNE) error mitigation to correct measurement errors.

Parameters: - prog (QProg): The quantum program to apply error mitigation. - shot (int): Number of shots for measurements. - expectations (List[str]): List of measurement expectations. - noise_strength (List[float]): List of noise strengths. - chip_id (int): Chip identifier. - task_name (str): Name of the QPanda Experiment task.

Returns: List[float]: List of corrected expectation values.

class pyqpanda.OriginService.QPilotOSMachine(name: str)[源代码]

Bases: QPilotOSService

This class can submit Quantum Program to PilotOS.

Attributes

PilotURLstr: Connect to the target PilotOS address.
PilotIpstr: PilotOS IP address.
PilotPortstr: PilotOS port.

get_expectation_result(task_id: str) → list[源代码]

get expectation task result

Parameters

task_idstr: expectation task id.

Returns

list: expectation task result.

get_qst_result(task_id: str) → list[源代码]

get qst task result through task_id

Parameters

task_idstr: the task_id you want to query

Returns

list: The list contains the information of qst task.

set_config(max_qubit: int = None, max_cbit: int = None) → None[源代码]

set Quantum Machine max Qubit and Cbit number function.

Parameters

max_qubitint: The Quantum Machine max available qubits.
max_cbitint: The Quantum Machine max available cbits.

Returns

None

Examples

>>> qm.set_config(12, 12)

init(url: str = None, log_cout: bool = False, api_key: str = None) → None[源代码]

Init Quantum Machine and connect to PilotOS.

Parameters

urlstr: The Quantum Machine address you want to connnect.
log_coutbool: Whether record execute log.
api_keystr: The unique certificate to login PilotOS, which can get from PilotOS WebSite.

Returns

None

Examples

>>> qm.init('PilotOS_url', True, 'your_api_key')

qAlloc_many(qubit_num: int = None) → list[源代码]

Get Qubits to construct Quantum Circuit.

Parameters

qubit_numint: The Qubits number you need to use in circuit.

Returns

List[Qubit]

Examples

>>> q = qm.qAlloc_many(6)

cAlloc_many(cbit_num: int = None) → list[源代码]

Get Cbits to construct Quantum Circuit.

Parameters

cbit_numint: The Cbits number you need to use in circuit.

Returns

List[ClassicalCondition]

Examples

>>> c = qm.cAlloc_many(6)

real_chip_measure(prog: List[str] | List[pyqpanda.QProg] | str | pyqpanda.QProg, shot=1000, chip_id=None, is_amend=True, is_mapping=True, is_optimization=True, specified_block=[], describe='') → list[源代码]

Using sync way to compute your Quantum Program .

Parameters

progUnion[List[str], List[QProg], str, QProg]: The quantum program you want to compute.
shotint: Repeate run quantum program times.
chip_idint: The quantum chip ID .
is_amendbool: Whether amend task result.
is_mappingbool: Whether mapping logical Qubit to Physical Qubit.
is_optimizationbool: Whether optimize your quantum program.
specified_blockList[int]: Your specifed Qubit block .
describestr: The detailed infomation to describe your quantum program, such as which kind of algorithm, what can this program compute.

Returns

list: a list of every single quantum program

Examples

>>> result = qm.real_chip_measure(prog_list, 1000, chip_id=1, is_mapping=True, describe="test1")
>>> print(result)
[{'00': 0.2456881582421773, '01': 0.2495193504871486, '10': 0.25044435129147546, '11': 0.25434813997919875}, {'00': 0.2456881582421773, '01': 0.2495193504871486, '10': 0.25044435129147546, '11': 0.25434813997919875}]

async_real_chip_measure(prog: List[str] | List[pyqpanda.QProg] | str | pyqpanda.QProg, shot=1000, chip_id=None, is_amend=True, is_mapping=True, is_optimization=True, specified_block=[], describe='') → str[源代码]

Using async way to compute your Quantum Program, then you need to query task result from task_id.

Parameters

progUnion[List[str], List[QProg], str, QProg]: The quantum program you want to compute.
shotint: Repeate run quantum program times.
chip_idint: The quantum chip ID .
is_amendbool: Whether amend task result.
is_mappingbool: Whether mapping logical Qubit to Physical Qubit.
is_optimizationbool: Whether optimize your quantum program.
specified_blockList[int]: Your specifed Qubit block .
describestr: The detailed infomation to describe your quantum program, such as which kind of algorithm, what can this program compute.

Returns

str: your task id which can query task result

Examples

This interface will return a string that will be used to query the results of the quantum program you just submitted.

>>> task_id = qm.async_real_chip_measure(prog_list, 1000, chip_id=1, is_mapping=True, describe="test1")
>>> print (task_id)
54C64205E2AF45D393FB5E6279E14984

real_chip_expectation(prog: pyqpanda.QProg | str, hamiltonian: str, qubits: List[int] = None, shot: int = None, chip_id: int = None, is_amend: bool = True, is_mapping: bool = True, is_optimization: bool = True, specified_block: List[int] = [], task_describe: str = '') → float[源代码]

submit Quantum expectation task, and get the expectation result.

Parameters

progUnion[QProg, str]: The quantum program you want to compute.
hamiltonianstr: Hamiltonian parameters.
qubitsList[int]: measurement qubit
shotint: Repeate run quantum program times.
chip_idint: The quantum chip ID .
is_amendbool: Whether amend task result.
is_mappingbool: Whether mapping logical Qubit to Physical Qubit.
is_optimizationbool: Whether optimize your quantum program.
specified_blockList[int]: Your specifed Qubit block .
task_describestr: The detailed infomation to describe your quantum program, such as which kind of algorithm, what can this program compute.

Returns

float: if success, return the expectation task result. Otherwise return empty.

async_real_chip_expectation(prog: pyqpanda.QProg | str, hamiltonian: str, qubits: List[int] = None, shot: int = None, chip_id: int = None, is_amend: bool = True, is_mapping: bool = True, is_optimization: bool = True, specified_block: List[int] = [], task_describe: str = '') → str[源代码]

async submit Quantum expectation task, and return the task id.

Parameters

progUnion[QProg, str]: The quantum program you want to compute.
hamiltonianstr: Hamiltonian parameters.
qubitsList[int]: measurement qubit
shotint: Repeate run quantum program times.
chip_idint: The quantum chip ID .
is_amendbool: Whether amend task result.
is_mappingbool: Whether mapping logical Qubit to Physical Qubit.
is_optimizationbool: Whether optimize your quantum program.
specified_blockList[int]: Your specifed Qubit block .
task_describestr: The detailed infomation to describe your quantum program, such as which kind of algorithm, what can this program compute.

Returns

str: return expectation task id, you need query task result by using task id.

async_real_chip_qst(prog: str | pyqpanda.QProg, shot=1000, chip_id=None, is_amend=True, is_mapping=True, is_optimization=True, specified_block=[], describe='') → list[源代码]

Using async way to compute QST task, then you need to query task result from task_id.

Parameters

progUnion[str, QProg]: The quantum program you want to compute.
shotint: Repeate run quantum program times.
chip_idint: The quantum chip ID .
is_amendbool: Whether amend task result.
is_mappingbool: Whether mapping logical Qubit to Physical Qubit.
is_optimizationbool: Whether optimize your quantum program.
specified_blockList[int]: Your specifed Qubit block .
describestr: The detailed infomation to describe your quantum program, such as which kind of algorithm, what can this program compute.

Returns

str: your task id which can query task result

query_task_state(task_id: str, file_path: str = None) → list[源代码]

Query task result from task_id.

task_idstr
The task id you want to query.

file_pathstr
If the parameter is not None, task result will be saved to target path.

list
Contains task state, task result, task error code, task error info, you can decide what to do with state and error code.

This interface will return a result list, contains: task state, probability result, error code, error info(if error code not equal to 0) You can decide whether to save the results of the task to a file by entering the second parameter or not, in particular, if you enter an empty string, the file will be saved in the current path
>>> result_list = qm.query_task_state(task_id, 'D:/python_test/result/')
>>> for i in result_list: print(i)
...
3
['{"key":["0","1"],"value":[0.5,0.5]}']
0
If you enter the second parameter a path to save task result json, the json string in file will be like: {

"taskId": "2258D6B6164F4F4FA8F85D1DA2F74370", "endTime": 1700466283544, "errCode": 0, "errInfo": "", "startTime": 1700466281627, "qProg": [

"["QINIT 72nCREG 72nX q[0]nH q[1]nMEASURE q[0]", "c[0]nMEASURE q[1]", "c[1]"", " "QINIT 72nCREG 72nX q[0]nH q[1]nMEASURE q[0]", "c[0]nMEASURE q[1]", "c[1]"]"

], "qProgLength": 6, "configuration": "{"shot":1000,"amendFlag":false,"mappingFlag":true,"circuitOptimization":true,"IsProbCount":false,"specified_block":[]}", "taskState": "3", "convertQProg": [

"[[{"RPhi":[2,270.0,90.0,0]},{"RPhi":[3,0.0,180.0,0]},{"Measure":[[2,3],30]}],[{"RPhi":[2,270.0,90.0,0]},{"RPhi":[3,0.0,180.0,0]},{"Measure":[[2,3],30]}]]"

], "mappingQProg": [

"QINIT 72

CREG 72 X q[0] H q[1] MEASURE q[0],c[0] MEASURE q[1],c[1]",

"QINIT 72

CREG 72 X q[0] H q[1] MEASURE q[0],c[0] MEASURE q[1],c[1]"

], "mappingQubit": [

"{"SrcQubits":[0,1],"TargetCbits":[0,1],"MappingQubits":[3,2]}", "{"SrcQubits":[0,1],"TargetCbits":[0,1],"MappingQubits":[3,2]}"

], "aioExecuteTime": 441, "queueTime": 0, "compileTime": 608, "totalTime": 1229, "aioCompileTime": 0, "aioPendingTime": 0, "aioMeasureTime": 0, "aioPostProcessTime": 0, "requiredCore": "0", "pulseTime": 60.0, "cirExecuteTime": 200000.0, "taskType": "0", "taskResult": [

"{"key":["00","01","10","11"],"value":[0.017,0.5,0.017,0.466]}", "{"key":["00","01","10","11"],"value":[0.018,0.474,0.025,0.483]}"

]

}

get_task_list_result(task_id: list, file_path: str = None) → list[源代码]

Get task result through task id list.

Parameters

task_idlist: The list of task id you want to query.
file_pathstr: If the parameter is not None, task result will be saved to target path.

Returns

list: This list contasins several dicts of task id and task result.

Examples

This interface will return a list, however, this list will not necessarily contain all the tasks queried, but will only return the results of the tasks that were queried to the completion of the calculation, and if the save path is set, these results will also be saved to a file.

>>> result_list = qm.get_task_list_result(task_id_list, 'D:/python_test/result/')
>>> print(result_list)
[{'task_id': '5D102BEED2714755B9B6AA082151F70E', 'task_result': ['{"key":["00","01","10","11"],"value":[0.25,0.25,0.25,0.25]}', '{"key":["00","01","10","11"],"value":[0.25,0.25,0.25,0.25]}']},
{'task_id': '18C163284EE043CAA691B201A9091891', 'task_result': ['{"key":["00","01","10","11"],"value":[0.25,0.25,0.25,0.25]}', '{"key":["00","01","10","11"],"value":[0.25,0.25,0.25,0.25]}']},
{'task_id': 'C929CE6E18374181A2E2297327CE6888', 'task_result': ['{"key":["00","01","10","11"],"value":[0.25,0.25,0.25,0.25]}', '{"key":["00","01","10","11"],"value":[0.25,0.25,0.25,0.25]}']}]

parse_probability_result(result_str: list) → list[源代码]

Parse async task probability result to a list contains dict.

Parameters

result_strstr: The json str contains task result key and value.

Returns

list

Examples

>>> result = qm.parse_probability_result(query_str)

quantum_chip_config_query(chip_ids: str) → str[源代码]

Get quantum chip config

Parameters

chip_idsstr: the json str contains chip id, it must be int or array, -1 represents all chips

Returns

str: return quantum chip configuration

Examples

>>> chipID_1 = {"ChipID":-1}
>>> chipID_2 = {"ChipID":[5,6,7]}
>>> config_1 = qm.quantum_chip_config_query(chipID_1)
>>> config_2 = qm.quantum_chip_config_query(chipID_2)
>>> print(config_1)
>>> print(config_2)

pyqpanda.OriginService

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`pyqpanda.OriginService`