A Hardware-Aware Heuristic for the Qubit Mapping Problem in the NISQ Era
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Abstract (arXiv)
Due to several physical limitations in the realisation of quantum hardware, today's quantum computers are qualified as Noisy Intermediate-Scale Quantum (NISQ) hardware. NISQ hardware is characterized by a small number of qubits (50 to a few hundred) and noisy operations. Moreover, current realisations of superconducting quantum chips do not have the ideal all-to-all connectivity between qubits but rather at most a nearest-neighbour connectivity. All these hardware restrictions add supplementary low-level requirements. They need to be addressed before submitting the quantum circuit to an actual chip. Satisfying these requirements is a tedious task for the programmer. Instead, the task of adapting the quantum circuit to a given hardware is left to the compiler. In this paper, we propose a Hardware-Aware mapping transition algorithm (HA) that takes the calibration data into account with the aim to improve the overall fidelity of the circuit. Evaluation results on IBM quantum hardware show that our HA approach can outperform the state of the art both in terms of the number of additional gates and circuit fidelity.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Oct 2020.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 1; 2024: 1; 2025: 0; 2026: 1.
5 papers.
- 2021Can Noise on Qubits Be Learned in Quantum Neural Network? A Case Study on QuantumFlowcited by 13
- 2024Quantum Circuit Synthesis and Compilation Optimization: Overview and Prospectscited by 8
- 2023Optimal Layout Synthesis for Quantum Circuits as Classical Planning (full version)cited by 7
- 2022Review of Ansatz Designing Techniques for Variational Quantum Algorithmscited by 3
- 2026QAP-Router: Tackling Qubit Routing as Dynamic Quadratic Assignment with Reinforcement Learningcited by 0
- 2026QAP-Router: Tackling Qubit Routing as Dynamic Quadratic Assignment with Reinforcement Learning12 May 2026
- 2024Quantum Circuit Synthesis and Compilation Optimization: Overview and Prospects30 Jun 2024
- 2023Optimal Layout Synthesis for Quantum Circuits as Classical Planning (full version)24 Apr 2023
- 2022Review of Ansatz Designing Techniques for Variational Quantum Algorithms7 Dec 2022
- 2021Can Noise on Qubits Be Learned in Quantum Neural Network? A Case Study on QuantumFlow8 Sep 2021