Advanced Scheduling Strategies for Distributed Quantum Computing Jobs
This work addresses scheduling problems for distributed quantum computing, offering incremental improvements over existing methods for researchers and engineers in quantum computing.
The paper tackles the challenge of scheduling distributed quantum computing jobs by considering quantum-specific constraints like QPU utilization and non-local gate rates, proposing strategies including reinforcement learning, and benchmarks them against traditional schedulers, showing improvements in makespan and resource efficiency.
Distributed quantum computing (DQC) is being actively investigated as a means of scaling the number of qubits across multiple connected quantum devices. 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 rate, 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.