Deadline-Aware Scheduling of Distributed Quantum Circuits in Near-Term Quantum Cloud

2025-12-04

AI Summary

This work addresses the distributed quantum circuit (DQC) scheduling problem in near- to mid-term quantum clouds, characterized by heterogeneous multi-QPU architectures, absence of dedicated quantum networks, and stringent execution deadline constraints. Method: We propose the first deadline-aware DQC scheduling framework, jointly optimizing subcircuit dependency resolution and shot allocation; introduce a lightweight line-cutting strategy to minimize inter-QPU communication overhead; and employ simulated annealing to compute deadline-aware schedules compatible with classical communication and local-operation architectures. Contribution/Results: To our knowledge, this is the first work to co-optimize user-specified deadlines with sampling resource allocation. Our approach significantly improves completion rates for urgent tasks: it increases service request handling by 12.8% under emergency conditions, boosts average throughput by 8.16–25.38%, and reduces overall execution span.

Technology Category

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

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

Distributed quantum computing (DQC) enables scalable quantum computations by distributing large quantum circuits on multiple quantum processing units (QPUs) in the quantum cloud. In DQC, after partitioning quantum circuits, they must be scheduled and executed on heterogenous QPUs while balancing latency, overhead, QPU communication resource limits. However, since fully functioning quantum communication networks have not been realized yet, near-term quantum clouds will only rely on local operations and classical communication settings between QPUs, without entangled quantum links. Additionally, existing DQC scheduling frameworks do not account for user-defined execution deadlines and adopt inefficient wire cutting techniques. Accordingly, in this work, a deadline aware DQC scheduling framework with efficient wire cutting for near-term quantum cloud is proposed. The proposed framework schedules partitioned quantum subcircuits while accounting for circuit deadlines and QPU capacity limits. It also captures dependencies between partitioned subcircuits and distributes the execution of the sampling shots on different QPUs to have efficient wire cutting and faster execution. In this regard, a deadline-aware circuit scheduling optimization problem is formulated, and solved using simulated annealing. Simulation results show a marked improvement over existing shot-agnostic frameworks under urgent deadlines, reaching a 12.8% increase in requests served before their deadlines. Additionally, the proposed framework serves 8.16% more requests, on average, compared to state-of-the-art dependency-agnostic baseline frameworks, and by 9.60% versus the dependency-and-shot-agnostic baseline, all while achieving a smaller makespan of the DQC execution. Moreover, the proposed framework serves 23.7%, 24.5%, and 25.38% more requests compared to greedy, list scheduling, and random schedulers, respectively.

Problem

• Scheduling distributed quantum circuits under user-defined execution deadlines
• Optimizing wire cutting and subcircuit dependencies for near-term quantum clouds
• Balancing latency, overhead, and QPU capacity without quantum communication links

Innovation

• Deadline-aware scheduling for distributed quantum circuits in cloud.
• Efficient wire cutting with shot distribution across multiple QPUs.
• Simulated annealing optimization for subcircuit dependencies and deadlines.