# Quantum Circuit Compiler for a Shuttling-Based Trapped-Ion Quantum Computer

arXiv: https://arxiv.org/abs/2207.01964 · HTML: https://mlsanity.com/paper/2207.01964/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 59 references that are in the map.
- First posted on arXiv 5 Jul 2022.

## Builds on (most cited first, 6 of 6)

- [Variational Quantum Algorithms](https://mlsanity.com/paper/2012.09265/) (2020)
- [Quantum Machine Learning](https://mlsanity.com/paper/1611.09347/) (2016)
- [On the qubit routing problem](https://mlsanity.com/paper/1902.08091/) (2019)
- [Optimized Compilation of Aggregated Instructions for Realistic Quantum Computers](https://mlsanity.com/paper/1902.01474/) (2019)
- [Optimising Clifford Circuits with Quantomatic](https://mlsanity.com/paper/1901.10114/) (2019)
- [Quantum Circuit Transformations with a Multi-Level Intermediate Representation Compiler](https://mlsanity.com/paper/2112.10677/) (2021)

## Built on by (most cited first, 2 of 2)

- [Inverse-Transpilation: Reverse-Engineering Quantum Compiler Optimization Passes from Circuit Snapshots](https://mlsanity.com/paper/2504.19113/) (2025)
- [Shuttling Compiler for Trapped-Ion Quantum Computers Based on Fine-Tuned Large Language Models](https://mlsanity.com/paper/2512.18021/) (2025)

## Abstract (arXiv)

The increasing capabilities of quantum computing hardware and the challenge of realizing deep quantum circuits require fully automated and efficient tools for compiling quantum circuits. To express arbitrary circuits in a sequence of native gates specific to the quantum computer architecture, it is necessary to make algorithms portable across the landscape of quantum hardware providers. In this work, we present a compiler capable of transforming and optimizing a quantum circuit targeting a shuttling-based trapped-ion quantum processor. It consists of custom algorithms set on top of the quantum circuit framework Pytket. The performance was evaluated for a wide range of quantum circuits and the results show that the gate counts can be reduced by factors up to 5.1 compared to standard Pytket and up to 2.2 compared to standard Qiskit compilation.