Advanced Scheduling Strategies for Distributed Quantum Computing Jobs

• URL: http://arxiv.org/abs/2602.24152v1
• Date: Fri, 27 Feb 2026 16:35:32 GMT
• Title: Advanced Scheduling Strategies for Distributed Quantum Computing Jobs
• Authors: Gongyu Ni, Davide Ferrari, Lester Ho, Michele Amoretti,
• Abstract summary: Scaling the number of qubits available across multiple quantum devices is an active area of research within distributed quantum computing (DQC)<n>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.
• Score: 1.4757786825685777
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Efficient Time-Aware Partitioning of Quantum Circuits for Distributed Quantum Computing [10.919776355400282]
  Distributed quantum computing (DQC) interconnects multiple smaller-scale quantum processing units (QPUs) to form a quantum network.<n>To minimize this communication overhead, DQC compilers must strategically partition quantum circuits by mapping logical qubits to distributed physical QPUs.<n>We propose a time-aware algorithm incrementally constructs a low-cost sequence of qubit assignments across successive time steps to minimize overall communication overhead.<n>Our proposed algorithm consistently achieves significantly lower communication costs than static baselines across varying circuit sizes, depths, and network topologies.
  arXiv  Detail & Related papers  (2026-03-04T14:43:10Z)
• Reinforcement Learning for Quantum Network Control with Application-Driven Objectives [53.03367590211247]
  Dynamic programming and reinforcement learning offer promising tools for optimizing control strategies.<n>We propose a novel RL framework that directly optimize non-linear, differentiable objective functions.<n>Our work comprises the first step towards non-linear objective function optimization in quantum networks with RL, opening a path towards more advanced use cases.
  arXiv  Detail & Related papers  (2025-09-12T18:41:10Z)
• Adaptive Job Scheduling in Quantum Clouds Using Reinforcement Learning [1.0542466736167886]
  Current quantum systems face critical bottlenecks, including limited qubit counts, brief coherence intervals, and high susceptibility to errors.<n>We introduce a simulation-based tool that supports distributed scheduling and concurrent execution of quantum jobs on networked QPUs connected via real-time classical channels.
  arXiv  Detail & Related papers  (2025-06-12T16:54:19Z)
• Resource Management and Circuit Scheduling for Distributed Quantum Computing Interconnect Networks [5.239117416189216]
  Distributed quantum computing (DQC) has emerged as a promising approach to overcome the scalability limitations of monolithic quantum processors.<n>This paper addresses resource management and circuit scheduling in such settings.<n>We propose circuit scheduling algorithms based on Mixed-Integer Linear Programming (MILP)
  arXiv  Detail & Related papers  (2024-09-19T11:39:46Z)
• Compiler for Distributed Quantum Computing: a Reinforcement Learning Approach [6.347685922582191]
  We introduce a novel compiler that prioritizes reducing the expected execution time by jointly managing the generation and routing of EPR pairs. We present a real-time, adaptive approach to compiler design, accounting for the nature of entanglement generation and the operational demands of quantum circuits. Our contributions are twofold: (i) we model the optimal compiler for DQC using a Markov Decision Process (MDP) formulation, establishing the existence of an optimal algorithm, and (ii) we introduce a constrained Reinforcement Learning (RL) method to approximate this optimal compiler.
  arXiv  Detail & Related papers  (2024-04-25T23:03:20Z)
• Adaptive User-Centric Entanglement Routing in Quantum Data Networks [5.421492821020181]
  Distributed quantum computing (DQC) holds immense promise in harnessing the potential of quantum computing by interconnecting multiple small quantum computers (QCs) through a quantum data network (QDN) establishing long-distance quantum entanglement between two QCs for quantum teleportation within the QDN is a critical aspect, and it involves entanglement routing. Existing approaches have mainly focused on optimizing entanglement performance for current entanglement connection (EC) requests. We present a novel user-centric entanglement routing problem that spans an extended period to maximize entanglement success rate while adhering to the user's budget constraint.
  arXiv  Detail & Related papers  (2024-04-13T17:20:00Z)
• Elastic Entangled Pair and Qubit Resource Management in Quantum Cloud Computing [73.7522199491117]
  Quantum cloud computing (QCC) offers a promising approach to efficiently provide quantum computing resources. The fluctuations in user demand and quantum circuit requirements are challenging for efficient resource provisioning. We propose a resource allocation model to provision quantum computing and networking resources.
  arXiv  Detail & Related papers  (2023-07-25T00:38:46Z)
• A Design Framework for the Simulation of Distributed Quantum Computing [2.969582361376132]
  Growing demand for large-scale quantum computers is pushing research on Distributed Quantum Computing (DQC) Recent experimental efforts have demonstrated some of the building blocks for such a design. DQC systems are clusters of quantum processing units connected by means of quantum network infrastructures.
  arXiv  Detail & Related papers  (2023-06-20T13:52:05Z)
• Scaling Limits of Quantum Repeater Networks [62.75241407271626]
  Quantum networks (QNs) are a promising platform for secure communications, enhanced sensing, and efficient distributed quantum computing. Due to the fragile nature of quantum states, these networks face significant challenges in terms of scalability. In this paper, the scaling limits of quantum repeater networks (QRNs) are analyzed.
  arXiv  Detail & Related papers  (2023-05-15T14:57:01Z)
• DQC$^2$O: Distributed Quantum Computing for Collaborative Optimization in Future Networks [54.03701670739067]
  We propose an adaptive distributed quantum computing approach to manage quantum computers and quantum channels for solving optimization tasks in future networks. Based on the proposed approach, we discuss the potential applications for collaborative optimization in future networks, such as smart grid management, IoT cooperation, and UAV trajectory planning.
  arXiv  Detail & Related papers  (2022-09-16T02:44:52Z)
• Optimal Stochastic Resource Allocation for Distributed Quantum Computing [50.809738453571015]
  We propose a resource allocation scheme for distributed quantum computing (DQC) based on programming to minimize the total deployment cost for quantum resources. The evaluation demonstrates the effectiveness and ability of the proposed scheme to balance the utilization of quantum computers and on-demand quantum computers.
  arXiv  Detail & Related papers  (2022-09-16T02:37:32Z)
• Decomposition of Matrix Product States into Shallow Quantum Circuits [62.5210028594015]
  tensor network (TN) algorithms can be mapped to parametrized quantum circuits (PQCs) We propose a new protocol for approximating TN states using realistic quantum circuits. Our results reveal one particular protocol, involving sequential growth and optimization of the quantum circuit, to outperform all other methods.
  arXiv  Detail & Related papers  (2022-09-01T17:08:41Z)
• Synergy Between Quantum Circuits and Tensor Networks: Short-cutting the Race to Practical Quantum Advantage [43.3054117987806]
  We introduce a scalable procedure for harnessing classical computing resources to provide pre-optimized initializations for quantum circuits. We show this method significantly improves the trainability and performance of PQCs on a variety of problems. By demonstrating a means of boosting limited quantum resources using classical computers, our approach illustrates the promise of this synergy between quantum and quantum-inspired models in quantum computing.
  arXiv  Detail & Related papers  (2022-08-29T15:24:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.