What question did this study set out to answer?

The aim is to improve task scheduling and resource allocation in noisy intermediate-scale quantum (NISQ) systems.

QRAP: A Quantum Resource Allocation Platform for Adaptive Scheduling Under Topology Constraints

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Randomized trial demonstrates improved task scheduling in quantum systems, indicating enhanced efficiency and adaptability.

The aim is to improve task scheduling and resource allocation in noisy intermediate-scale quantum (NISQ) systems.
Proposed a reinforcement learning framework for scheduling in distributed NISQ systems.
Formulated the scheduling as a constrained optimization problem within a Markov decision process.
Implemented deep Q-network and proximal policy optimization agents, comparing against heuristic and random methods.
Proximal policy optimization consistently outperformed DQN and heuristic methods.
Achieved higher task completion rates with fewer deadline violations.
Demonstrated robust adaptation across different reward configurations.

Guo et al. (2026) studied this question.

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View Full Paper

Ask AI

Bookmark

Authors

Randomized trial demonstrates improved task scheduling in quantum systems, indicating enhanced efficiency and adaptability.

The aim is to improve task scheduling and resource allocation in noisy intermediate-scale quantum (NISQ) systems.
Proposed a reinforcement learning framework for scheduling in distributed NISQ systems.
Formulated the scheduling as a constrained optimization problem within a Markov decision process.
Implemented deep Q-network and proximal policy optimization agents, comparing against heuristic and random methods.
Proximal policy optimization consistently outperformed DQN and heuristic methods.
Achieved higher task completion rates with fewer deadline violations.
Demonstrated robust adaptation across different reward configurations.

Guo et al. (2026) studied this question.

View Full Paper

Ask AI

Bookmark