Towards Scalable Quantum Simulation: Distributed Circuit Cutting for Hybrid Quantum-HPC Systems

Abstract

As quantum computing advances, practical deployment of quantum algorithms remains hindered by hardware limitations such as restricted qubit counts and and limited connectivity. Circuit cutting has emerged as a promising strategy to extend quantum computations beyond these hardware constraints by decomposing large circuits into smaller subcircuits that can be executed individually and recombined through classical post-processing. This master thesis presents Qdislib, an open-source software library that integrates quantum circuit cutting with high-performance computing (HPC) infrastructure to enable scalable and hybrid quantum-classical workflows. Qdislib builds on PyCOMPSs, a task-based runtime system, to orchestrate the parallel execution of subcircuits across heterogeneous resources, including CPUs, GPUs, and quantum processors (QPUs). The library supports both wire cutting and gate cutting techniques and introduces an automated cut selection algorithm, FindCut, to optimize circuit partitioning based on user-defined constraints. Benchmarking is performed on Hardware-Efficient Ansatz (HEA) and Random Circuit (RC) workloads, evaluating execution on MareNostrum 5 and IBM Quantum Cloud. Results demonstrate strong scalability for classical simulations and hybrid execution, achieving near-linear speedups on up to 64 compute nodes and successfully integrating local and remote QPUs. Qdislib thus provides a practical and extensible framework for distributed quantum simulation, paving the way for scalable quantum computation in heterogeneous environments