Distributed Quantum Circuit Cutting for Hybrid Quantum-Classical High-Performance Computing

TL;DR

This work tackles the scalability gap between growing quantum circuit sizes and hardware-limited qubit counts by introducing Qdislib, a distributed, graph-based library for quantum circuit cutting that enables hybrid quantum-classical HPC workflows. It implements both wire cutting and gate cutting, supports quasi-probabilistic reconstruction, and uses the FindCut algorithm to optimize partitioning under user constraints, all orchestrated via PyCOMPSs to run subcircuits across CPUs, GPUs, and QPUs. The evaluation on heterogeneous infrastructure with Hardware Efficient Ansatz and Random Circuits demonstrates near-linear speedups (e.g., up to $54.4\times$) and effective handling of large circuits through parallel execution, including hybrid CPU/GPU/QPU configurations and cloud access. The results underscore the practical potential of circuit cutting for scalable quantum-classical applications and position Qdislib as a flexible, extensible platform for future quantum HPC research and development, including integration with additional backends and optimizations for cut reduction and hardware-aware scheduling.

Abstract

Most quantum computers today are constrained by hardware limitations, particularly the number of available qubits, causing significant challenges for executing large-scale quantum algorithms. Circuit cutting has emerged as a key technique to overcome these limitations by decomposing large quantum circuits into smaller subcircuits that can be executed independently and later reconstructed. In this work, we introduce Qdislib, a distributed and flexible library for quantum circuit cutting, designed to seamlessly integrate with hybrid quantum-classical high-performance computing (HPC) systems. Qdislib employs a graph-based representation of quantum circuits to enable efficient partitioning, manipulation and execution, supporting both wire cutting and gate cutting techniques. The library is compatible with multiple quantum computing libraries, including Qiskit and Qibo, and leverages distributed computing frameworks to execute subcircuits across CPUs, GPUs, and quantum processing units (QPUs) in a fully parallelized manner. We present a proof of concept demonstrating how Qdislib enables the distributed execution of quantum circuits across heterogeneous computing resources, showcasing its potential for scalable quantum-classical workflows.

Figures (13)

• Figure 1: Example of a quantum circuit partitioned.
• Figure 2: Decomposition for cutting a Controlled-Z gate. Six subcircuits are required to reconstruct this gate mitarai2021constructing.
• Figure 3: Circuit Cutting Qdislib workflow.
• Figure 4: FindCut workflow in Qdislib.
• Figure 5: Hybrid execution schema.