DistributedEstimator: Distributed training of quantum neural networks via circuit cutting

Abstract

Circuit cutting decomposes a large quantum circuit into smaller subcircuits that are executed independently; the original circuit’s expectation values are then recovered by classically combining the measured subcircuit outcomes. While prior work characterises cutting overhead in terms of subcircuit counts and sampling complexity, its end-to-end impact on iterative, estimator-driven training pipelines remains insufficiently measured from a systems perspective. We propose DistributedEstimator, a cut-aware estimator execution pipeline that treats circuit cutting as a staged distributed workload. Each estimator query is instrumented across four phases: partitioning, subexperiment generation, par..