QFOR: A Fidelity-aware Orchestrator for Quantum Computing Environments using Deep Reinforcement Learning

TL;DR

QFOR leverages deep reinforcement learning to dynamically orchestrate quantum tasks across heterogeneous cloud quantum processors, significantly improving fidelity while maintaining execution time.

Contribution

This paper introduces QFOR, a novel fidelity-aware quantum task orchestration framework using deep reinforcement learning for heterogeneous quantum cloud environments.

Findings

Achieves 29.5-84% fidelity improvement over heuristics

Balances fidelity and execution time effectively

Demonstrates adaptability to hardware noise and operational priorities

Abstract

Quantum cloud computing enables remote access to quantum processors, yet the heterogeneity and noise of available quantum hardware create significant challenges for efficient resource orchestration. These issues complicate the optimization of quantum task allocation and scheduling, as existing heuristic methods fall short in adapting to dynamic conditions or effectively balancing execution fidelity and time. Here, we propose QFOR, a Quantum Fidelity-aware Orchestration of tasks across heterogeneous quantum nodes in cloud-based environments using Deep Reinforcement learning. We model the quantum task orchestration as a Markov Decision Process and employ the Proximal Policy Optimization algorithm to learn adaptive scheduling policies, using IBM quantum processor calibration data for noise-aware performance estimation. Our configurable framework balances overall quantum task execution fidelity and time, enabling adaptation to different operational priorities. Extensive evaluation demonstrates that QFOR is adaptive and achieves significant performance with 29.5-84% improvements in relative fidelity performance over heuristic baselines. Furthermore, it maintains comparable quantum execution times, contributing to cost-efficient use of quantum computation resources.