# Designing High-Fidelity Single-Shot Three-Qubit Gates: A Machine Learning Approach

arXiv: https://arxiv.org/abs/1511.08862 · HTML: https://mlsanity.com/paper/1511.08862/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Nov 2015.

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

- [Quantum Stream Learning](https://mlsanity.com/paper/2112.06628/) (2021)
- [Data-Driven Qubit Characterization and Optimal Control using Deep Learning](https://mlsanity.com/paper/2601.18704/) (2026)

## Abstract (arXiv)

Three-qubit quantum gates are key ingredients for quantum error correction and quantum information processing. We generate quantum-control procedures to design three types of three-qubit gates, namely Toffoli, Controlled-Not-Not and Fredkin gates. The design procedures are applicable to a system comprising three nearest-neighbor-coupled superconducting artificial atoms. For each three-qubit gate, the numerical simulation of the proposed scheme achieves 99.9% fidelity, which is an accepted threshold fidelity for fault-tolerant quantum computing. We test our procedure in the presence of decoherence-induced noise as well as show its robustness against random external noise generated by the control electronics. The three-qubit gates are designed via the machine learning algorithm called Subspace-Selective Self-Adaptive Differential Evolution (SuSSADE).