On the optimality of quantum circuit initial mapping using reinforcement learning

Norhan Elsayed Amer*, Walid Gomaa, Keiji Kimura, Kazunori Ueda, Ahmed El-Mahdy

*この研究の対応する著者

研究成果: Article査読

抄録

Quantum circuit optimization is an inevitable task with the current noisy quantum backends. This task is considered non-trivial due to the varying circuits’ complexities in addition to hardware-specific noise, topology, and limited connectivity. The currently available methods either rely on heuristics for circuit optimization tasks or reinforcement learning with complex unscalable neural networks such as transformers. In this paper, we are concerned with optimizing the initial logical-to-physical mapping selection. Specifically, we investigate whether a reinforcement learning agent with simple scalable neural network is capable of finding a near-optimal logical-to-physical mapping, that would decrease as much as possible additional CNOT gates, only from a fixed-length feature vector. To answer this question, we train a Maskable Proximal Policy Optimization agent to progressively take steps towards a near-optimal logical-to-physical mapping on a 20-qubit hardware architecture. Our results show that our agent coupled with a simple routing evaluation is capable of outperforming other available reinforcement learning and heuristics approaches on 12 out of 19 test benchmarks, achieving geometric mean improvements of 2.2% and 15% over the best available related work and two heuristics approaches, respectively. Additionally, our neural network model scales linearly as the number of qubits increases.

本文言語English
論文番号19
ジャーナルEPJ Quantum Technology
11
1
DOI
出版ステータスPublished - 2024 12月

ASJC Scopus subject areas

  • 制御およびシステム工学
  • 原子分子物理学および光学
  • 凝縮系物理学
  • 電子工学および電気工学

フィンガープリント

「On the optimality of quantum circuit initial mapping using reinforcement learning」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル