FragQC: An efficient quantum error reduction technique using quantum circuit fragmentation

• Published in: The Journal of systems and software Vol. 214; p. 112085
• Main Authors: Basu, Saikat, Das, Arnav, Saha, Amit, Chakrabarti, Amlan, Sur-Kolay, Susmita
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.08.2024
• Subjects: Circuit cutting; Genetic algorithm; Graph partitioning; Hybrid quantum systems; Quantum annealing; Quantum circuit fragmentation; Quantum error; Graph partitioning; Genetic algorithm; Quantum circuit fragmentation; Quantum annealing; Hybrid quantum systems; Quantum error; Circuit cutting
• ISSN: 0164-1212; 1873-1228
• DOI: 10.1016/j.jss.2024.112085

Quantum computers must meet extremely stringent qualitative and quantitative requirements on their qubits in order to solve real-life problems. Quantum circuit fragmentation techniques divide a large quantum circuit into a number of sub-circuits that can be executed on the smaller noisy quantum hardware available. However, the process of quantum circuit fragmentation involves finding an ideal cut that has exponential time complexity and also the classical post-processing required to reconstruct the output. In this paper, we represent a quantum circuit using a weighted graph and propose a novel classical graph partitioning algorithm for selecting an efficient fragmentation that reduces the entanglement between the sub-circuits along with balancing the estimated error in each sub-circuit. We also demonstrate a comparative study of different classical and quantum approaches to graph partitioning for finding such a cut. We present FragQC, a software tool that cuts a quantum circuit into sub-circuits when its error probability exceeds a certain threshold. With this proposed approach, we achieve an increase in fidelity of 13.38% compared to direct execution without cutting the circuit, and 7.88% over the state-of-the-art ILP-based method for the benchmark circuits. The code for FragQC is available at https://github.com/arnavdas88/FragQC. •Innovative graph-based quantum circuit representation capturing errors.•Efficient error-aware circuit partitioning.•Efficient quantum circuit fragmentation tool: FragQC.•FragQC enhances fidelity beyond current circuit cutting methods.•FragQC is adaptable to any quantum hardware, conquering ML-based circuit cutting methods.