# Optimization of Quantum Circuit Mapping using Gate Transformation and Commutation

arXiv: https://arxiv.org/abs/1907.02686 · HTML: https://mlsanity.com/paper/1907.02686/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jul 2019.

## Built on by (most cited first, 6 of 6)

- [Noisy intermediate-scale quantum (NISQ) algorithms](https://mlsanity.com/paper/2101.08448/) (2021)
- [A Survey on Quantum Machine Learning: Current Trends, Challenges, Opportunities, and the Road Ahead](https://mlsanity.com/paper/2310.10315/) (2023)
- [Qubit Routing using Graph Neural Network aided Monte Carlo Tree Search](https://mlsanity.com/paper/2104.01992/) (2021)
- [Optimal Layout Synthesis for Deep Quantum Circuits on NISQ Processors with 100+ Qubits](https://mlsanity.com/paper/2403.11598/) (2024)
- [AlphaCNOT: Learning CNOT Minimization with Model-Based Planning](https://mlsanity.com/paper/2604.13812/) (2026)
- [Profiling quantum circuits for their efficient execution on single- and multi-core architectures](https://mlsanity.com/paper/2407.12640/) (2024)

## Abstract (arXiv)

This paper addresses quantum circuit mapping for Noisy Intermediate-Scale Quantum (NISQ) computers. Since NISQ computers constraint two-qubit operations on limited couplings, an input circuit must be transformed into an equivalent output circuit obeying the constraints. The transformation often requires additional gates that can affect the accuracy of running the circuit. Based upon a previous work of quantum circuit mapping that leverages gate commutation rules, this paper shows algorithms that utilize both transformation and commutation rules. Experiments on a standard benchmark dataset confirm the algorithms with more rules can find even better circuit mappings compared with the previously-known best algorithms.