Advanced Scheduling Strategies for Distributed Quantum Computing Jobs

TL;DR

This paper explores advanced scheduling strategies for distributed quantum computing, addressing quantum-specific constraints and proposing heuristics and reinforcement learning methods to optimize job scheduling and resource utilization.

Contribution

It introduces novel scheduling strategies including heuristics and reinforcement learning tailored for distributed quantum computing environments.

Findings

Heuristic strategies improve QPU utilization and reduce makespan.

Reinforcement learning-based scheduling outperforms traditional FIFO and LIST methods.

Strategies adapt effectively to varying network conditions and job types.

Abstract

Distributed quantum computing (DQC) is being actively investigated as a means of scaling the number of qubits across multiple connected quantum devices. This includes quantum circuit compilation and execution management on multiple quantum devices in the network. The latter aspect is very challenging because, while reducing the makespan of job batches remains a relevant objective, novel quantum-specific constraints must be considered, including QPU utilization, non-local gate rate, and the latency associated with queued DQC jobs. In this work, a range of scheduling strategies is proposed, simulated, and evaluated, including heuristics that prioritize resource maximization for QPU utilization, node selection based on heterogeneous network connectivity, asynchronous node release upon job completion, and a scheduling strategy based on reinforcement learning with proximal policy optimization. These approaches are benchmarked against traditional FIFO and LIST schedulers under varying DQC job types and network conditions for the allocation of DQC jobs to devices within a network.