quapp-common 0.0.11.dev10

Quapp common library supporting Quapp Platform for Quantum Computing

quapp-common

Quapp common library supporting Quapp Platform for Quantum Computing.

Overview

quapp-common is a Python library designed to support the Quapp Platform for Quantum Computing by providing common utilities, configurations, and abstractions for working with quantum providers and devices. Recent improvements add first-class asynchronous job processing with a cross-process JobManager, background task execution, and standardized result models for immediate client responses while work continues in the background.

Features

• Provider and device factory for quantum computing platforms.
• Logging and configuration utilities with improved and detailed log messages.
• Support for AWS Braket, OQC Cloud, Qiskit, PennyLane, DWave Ocean, and Quapp quantum simulators.
• Refactored classes and utilities to remove tenant-specific request, response, and promise classes.
• Standardized naming by renaming ProjectHeader to CustomHeader.
• Enhanced error handling and job metadata update mechanisms.
• Simplified and cleaner HTTP request/response logging and URL parsing utilities.
• Asynchronous job processing:
  • AsyncInvocationTask to run handlers in a background thread pool and return immediate responses.
  • JobManager for cross-process job registry with atomic add/update/get and pub-sub updates.
  • Standard Event/Result (Success, Error) models for consistent async responses.
  • Scheduler integration via callback URL; local updates are patched to the scheduler.
  • Safety: updates to jobs already DONE/FAILED are ignored to prevent post-completion mutations.

Installation

Install via pip:

pip install quapp-common

Notes:

• From version 0.0.11.dev7, starlette is a direct dependency to support background execution helpers.

Recently Changes Highlights

• Version bump to 0.0.11.dev7 and dependency update (add starlette).
• Introduce asynchronous job processing primitives:
  • AsyncInvocationTask for background execution with immediate client response.
  • Event and standardized Result models (Success, Error).
• Introduce JobManager for cross-process job management and update publication.
• Integrate JobManager into Request to register jobs and carry scheduler callback URL.
• Modularize update_job_metadata with clearer helpers; patch scheduler on local state changes.
• Fix: ignore updates for jobs that are already DONE or FAILED.
• Improve logging and error handling (use full tracebacks, cleaner logs).

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For detailed usage and API references, please refer to the in-code documentation or contact the maintainers.