Resource Management and Circuit Scheduling for Distributed Quantum Computing Interconnect Networks

• URL: http://arxiv.org/abs/2409.12675v4
• Date: Mon, 01 Sep 2025 16:03:36 GMT
• Title: Resource Management and Circuit Scheduling for Distributed Quantum Computing Interconnect Networks
• Authors: Sima Bahrani, Romerson D. Oliveira, Juan Marcelo Parra-Ullauri, Rui Wang, Dimitra Simeonidou,
• Abstract summary: 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)
• Score: 5.239117416189216
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Distributed quantum computing (DQC) has emerged as a promising approach to overcome the scalability limitations of monolithic quantum processors in terms of computational capability. However, realising the full potential of DQC requires effective resource management and circuit scheduling. This involves efficiently assigning each circuit to a subset of quantum processing units (QPUs), based on factors such as their computational power and connectivity. In heterogeneous DQC networks with arbitrary connectivity topologies and non-identical QPUs, this becomes a complex challenge. This paper addresses resource management and circuit scheduling in such settings, with a focus on computing resource allocation in a quantum data center. We propose circuit scheduling algorithms based on Mixed-Integer Linear Programming (MILP). Our MILP model accounts for errors arising from inter-QPU communication. In particular, the proposed schemes consider key factors, including network topology, QPU capacities, and quantum circuit structure, to make efficient scheduling and allocation decisions. Simulation results demonstrate that our proposed algorithms significantly improve circuit execution time and scheduling efficiency (measured by makespan and throughput), while also reducing inter-QPU communication overhead, compared to a baseline random allocation strategy. This work provides valuable insights into resource management strategies for scalable and heterogeneous DQC systems.

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