Execution Flow
------------

The following figure shows the end-to-end workflow of quantum computing tasks in the Pilot system.

.. figure:: image/calculate_process-3.png
    :align: center
    :width: 100%

**Task Submission:**
Users can submit quantum computing tasks through the following methods:

- **QPandaLite**: A Python SDK built on top of the Pilot Web management interface (via Nginx service);
- **PilotOSMachine**: Available in Python and C++ versions;
- **Origin Cloud Platform**: A public HTTP API for external access.

All tasks are received and managed persistently by the QCloudServer layer.

**Task Preprocessing and Routing:**
For specialized tasks (such as QST, Expectation Estimation, EM Computation), QCloudServer forwards them to dedicated computing services for preprocessing before calling the Pilot kernel through the system API.

**Task Orchestration in Pilot:**

- The OSServerManager module acts as the central coordinator, managing internal modules and maintaining task queues.
- The status of each task is tracked through a shared state mapping table. As tasks flow between modules, their status updates are aggregated in OSServerManager.
- After a task is completed (success, failure, or cancellation), all status data is transferred to QCloudServer for persistent storage and removed from OSServerManager—ensuring that OSServerManager only retains the status of active tasks.
- OSServerManager also provides resource estimation functionality, predicting the required number of qubits, circuit depth, and runtime to support scheduling decisions.

**Scheduling:**
Estimated tasks enter the scheduling queue. The scheduler allocates resources based on:

- Task resourc / execution time requirements;
- Current backend availability.

Supported strategies include:

- First-Come, First-Served (FCFS);
- Highest Response Ratio Next (HRRN).

These can be configured through Pilot's configuration files.

**Compilation and Execution:**
Scheduled tasks are compiled into backend-specific instructions through optimization channels.
The backend service then:

- Distributes the task to the target quantum hardware (supporting multiple architectures);
- Obtains results and backend status;
- Applies result correction algorithms to mitigate hardware noise and improve accuracy.

This streamlined architecture ensures stability, scalability, and efficient resource utilization across diverse quantum computing workloads.