Advanced Scheduling Strategies for Distributed Quantum Computing Jobs

2026-02-27

AI Summary

This work addresses the challenge of cross-device task scheduling in distributed quantum computing by proposing a novel scheduling method that incorporates quantum-specific constraints, including QPU utilization, non-local gate density, and queuing delay. The core innovation lies in the first application of Proximal Policy Optimization (PPO) reinforcement learning to distributed quantum computing (DQC) scheduling, augmented with a heterogeneous network-aware node selection mechanism and an asynchronous node release strategy triggered upon job completion. Experimental results demonstrate that the proposed approach significantly outperforms conventional schedulers such as FIFO and LIST across diverse job types and network conditions, achieving reduced job completion times while simultaneously improving QPU utilization and overall system throughput.

Technology Category

• Machine Learning: Quantum Machine Learning
• Planning, Routing, and Scheduling: Learning for Planning and Scheduling
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Scaling the number of qubits available across multiple quantum devices is an active area of research within distributed quantum computing (DQC). 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 density, 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.

Problem

• Distributed Quantum Computing
• Job Scheduling
• QPU Utilization
• Non-local Gate Density
• Makespan

Innovation

• distributed quantum computing
• quantum job scheduling
• reinforcement learning
• QPU utilization
• heterogeneous network