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

2025-05-02

AI Summary

To address the limitation of current NISQ hardware—insufficient qubit counts for executing large-scale quantum circuits—this work proposes a distributed graph-based representation framework supporting both wire cutting and gate cutting, enabling automatic decomposition and heterogeneous reconstruction of large quantum circuits. Methodologically, it integrates graph neural network–driven circuit modeling, full-stack parallelized quantum-classical co-execution (across CPU/GPU/QPU), multi-backend runtime adaptation (Qiskit/Qibo), and a mixed-precision tensor reconstruction algorithm. The key contribution is the first unified graph framework that natively supports dual-mode circuit cutting and achieves platform-agnostic, library-independent scheduling. Experimental evaluation on hundred-qubit circuits demonstrates a 42% reduction in communication overhead and a 3.8× improvement in end-to-end throughput, significantly extending the practical applicability of existing NISQ devices.

Technology Category

• Machine Learning: Quantum Machine Learning
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Distributed Search

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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.

Problem

• Overcoming qubit limitations in quantum computers
• Distributed execution of large quantum circuits
• Hybrid quantum-classical HPC integration

Innovation

• Distributed quantum circuit cutting library
• Graph-based circuit partitioning and execution
• Hybrid quantum-classical HPC integration