Maria Gragera Garces

Maria Gragera Garces

Hypergraph partitioning is a central component of distributed quantum computing (DQC) compilers. However, due to the limited size of available quantum benchmark suites, many partitioning studies rely on random quantum circuits as evaluation workloads. In this work, we investigate whether such benchmarking practices provide a faithful assessment of partitioner performance. We evaluate a diverse set of state-of-the-art hypergraph partitioning strategies across three circuit origins: real algorithmic circuits, structured generated circuits, and fully random circuits. Our results show that random circuits significantly distort partitioning evaluation. They inflate cut costs, alter scaling trends across QPU counts and circuit sizes, and change the relative ranking of partitioning strategies. In contrast, structured generated circuits exhibit substantially lower distortion, more closely approximating real workload behaviour in cost, scaling, and strategy rankings. These findings demonstrate that benchmark selection directly influences methodological conclusions in DQC research and that random circuits may provide misleading guidance for compiler design.

Maria Gragera Garces, Majid Haghparast

As distributed quantum architectures begin to emerge, understanding the interaction between quantum circuit optimisation and circuit partitioning becomes increasingly important. In this work, we study how circuit optimisation influences distributed quantum workloads under system-level trade-offs. We compare three compilation strategies (global optimisation, local optimisation, and a hybrid approach) across a large benchmark suite of quantum algorithms. Using telegate-based partitioning, we evaluate the resulting distributed circuits in terms of gate counts, circuit depth, the number of induced non-local gates, and compilation overhead, thereby approximating computational, communication, and classical preprocessing costs. Our results show that circuit optimisation does not uniformly benefit distributed execution. Global optimisation minimises computational resources and achieves the lowest compilation overhead. Local optimisation can reduce communication cost even though it is not explicitly communication-aware. The hybrid strategy can simultaneously reduce both computational and communication overhead, but at the expense of significantly increased compilation time.