Resource Management and Circuit Scheduling for Distributed Quantum Computing Interconnect Networks

• URL: http://arxiv.org/abs/2409.12675v3
• Date: Wed, 21 May 2025 13:29:02 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: We propose circuit scheduling and resource allocation algorithms that combine methods with a Mixed-Integer Linear Programming (MILP) formulation.<n>We show that our approach significantly improves circuit execution time and resource utilisation, measured by makespan, throughput, and QPU usage.
• Score: 4.0985912998349345
• 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 computing capability. However, realising the full potential of DQC requires effective resource management and circuit scheduling. This involves efficiently assigning each circuit to an optimal subset of quantum processing units (QPUs), based on factors such as their computational power and connectivity. In heterogeneous DQC networks with arbitrary topologies and non-identical QPUs, this becomes a complex challenge. This paper addresses resource management in such settings, with a focus on computing resource allocation in a quantum data center. We propose circuit scheduling and resource allocation algorithms that combine heuristic methods with a Mixed-Integer Linear Programming (MILP) formulation. Our MILP model accounts for infidelities arising from inter-QPU communication. The algorithms consider key factors including network topology, QPU characteristics, and quantum circuit structure to make efficient scheduling and allocation decisions. Simulation results demonstrate that our approach significantly improves circuit execution time and resource utilisation, measured by makespan, throughput, and QPU usage, while also reducing inter-QPU communication, 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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