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Trapped Ion Quantum Computing

Graph-based identification of qubit network (GidNET) for qubit reuse

arXiv
Authors: Gideon Uchehara, Tor M. Aamodt, Olivia Di Matteo

Year

2024

Paper ID

38222

Status

Preprint

Abstract Read

~2 min

Abstract Words

203

Citations

N/A

Abstract

Quantum computing introduces the challenge of optimizing quantum resources crucial for executing algorithms within the limited qubit availability of current quantum architectures. Existing qubit reuse algorithms face a trade-off between optimality and scalability, with some achieving optimal reuse but limited scalability due to computational complexities, while others exhibit reduced runtime at the expense of optimality. This paper introduces GidNET (Graph-based Identification of qubit NETwork), an algorithm for optimizing qubit reuse in quantum circuits. By analyzing the circuit's Directed Acyclic Graph (DAG) representation and its corresponding candidate matrix, GidNET identifies higher-quality pathways for qubit reuse more efficiently. Through a comparative study with established algorithms, notably QNET [1], GidNET not only achieves a consistent reduction in compiled circuit widths by a geometric mean of 4.4%, reaching up to 21% in larger circuits, but also demonstrates enhanced computational speed and scaling, with average execution time reduction of 97.4% (i.e., 38.5X geometric mean speedup) and up to 99.3% (142.9X speedup) across various circuit sizes. Furthermore, GidNET consistently outperforms Qiskit in circuit width reduction, achieving an average improvement of 59.3%, with maximum reductions of up to 72% in the largest tested circuits. These results demonstrate GidNET's ability to improve circuit width and runtime, offering a solution for quantum computers with limited numbers of qubits.

Why This Paper Matters

  • This paper contributes to the Trapped-Ion Quantum Computing research area in the Quantum Articles archive.
  • It adds a 2024 reference point for readers tracking recent quantum research.
  • Quantum computing introduces the challenge of optimizing quantum resources crucial for executing algorithms within the limited qubit availability of current quantum architectures.

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