`pyqpanda.OriginService.PilotOSMachine`

Module Contents

Classes

QPilotOSMachine

This class can submit Quantum Program to PilotOS.

class pyqpanda.OriginService.PilotOSMachine.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.PilotOSMachine

Module Contents

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`pyqpanda.OriginService.PilotOSMachine`