pilot_service Module

The pyqpanda3.pilot_service module provides QPilot OS integration for advanced quantum cloud workflows. It connects users to virtual machine clusters, supercomputers, and real quantum computing devices through the QPilot OS platform.

Overview

The pilot_service module offers a lower-level interface compared to the qcloud module, providing fine-grained control over quantum job submission, task building, result parsing, and backend communication. It is designed for users who need advanced features such as:

• Direct task message construction and submission
• Asynchronous job execution with polling
• Quantum State Tomography (QST) tasks
• Expectation value computation on real chips
• Error mitigation and noise learning
• TCP-based result retrieval

The typical workflow is:

1. Create a QPilotService instance with the QPilot OS server URL and credentials.
2. Submit quantum programs using run() or async_run().
3. Query task status with query_result() or query_task_state_vec().
4. Parse results using parse_task_result() or parse_probability_result().

Classes

Class	Description
QPilotService	Main service class for interacting with QPilot OS backends
PilotNoiseParams	Noise parameters for pilot-based simulations

Enumerations

Enum	Description
ErrorCode	Error codes returned by QPilot OS operations

Quick Start

from pyqpanda3.pilot_service import QPilotService
from pyqpanda3.core import QProg, QCircuit, H, CNOT, measure

# Connect to QPilot OS
service = QPilotService(
    url="https://qpilot.originquantum.cn",
    api_key="your_api_key"
)

# Create a quantum program
circuit = QCircuit(2)
circuit << H(0) << CNOT(0, 1)
prog = QProg()
prog << circuit << measure([0, 1], [0, 1])

# Run synchronously
result_str = service.run(prog, shot=1000, chip_id="simulation")

# Or run asynchronously
task_id = service.async_run(prog, shot=1000, chip_id="simulation")

# Query the result
state, results, err_code, err_info = service.query_result(task_id)

See Also

• qcloud Module -- Higher-level cloud service interface