FragQC: An Efficient Quantum Error Reduction Technique using Quantum Circuit Fragmentation

TL;DR

FragQC introduces a novel graph partitioning method for quantum circuit fragmentation, significantly reducing error and entanglement, thereby improving fidelity on noisy quantum hardware.

Contribution

It proposes a new classical graph partitioning algorithm for efficient quantum circuit fragmentation, enhancing fidelity and reducing complexity compared to existing methods.

Findings

Achieves 14.83% fidelity increase over uncut circuits.

Outperforms ILP-based methods by 8.45% in fidelity.

Demonstrates effectiveness on benchmark quantum circuits.

Abstract

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 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 over different classical and quantum approaches of graph partitioning for finding such a cut. We present {\it 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 of fidelity by 14.83\% compared to direct execution without cutting the circuit, and 8.45\% over the state-of-the-art ILP-based method, for the benchmark circuits.