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Distributed Quantum Circuit Cutting for Hybrid Quantum-Classical High-Performance Computing
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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.
Forward citations
Cited by 3 Pith papers
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MOSAIQC: Mixed-topology-aware Optimization for Scalable Approximate noise-Informed Quantum circuit Cutting
A heuristic circuit-cutting framework combining METIS, tabu search, and quadratic assignment reports faster runtimes and fewer cuts than Qiskit's add-on on tested benchmarks.
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MPStab: an hybrid stabilizers tensor-network quantum circuit simulator
MPStab implements hybrid stabilizer–MPO circuit simulation and shows it outperforms pure tensor networks on Clifford-heavy circuits with moderate magic at matched bond dimension.
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Scaling Portfolio Diversification with Quantum Circuit Cutting Techniques
QuantCut automatically selects gate cuts and reconstructs expectation values, enabling a 71-qubit QAOA portfolio diversification run, though it does not beat a classical heuristic.
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