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

TL;DR

This paper introduces a machine learning-based method to design high-fidelity three-qubit quantum gates, achieving near-threshold fidelities suitable for fault-tolerant quantum computing in superconducting systems.

Contribution

It develops a novel control scheme using SuSSADE to generate three-qubit gates with high fidelity, robustness, and applicability to superconducting quantum systems.

Findings

Achieved 99.9% fidelity for three-qubit gates

Demonstrated robustness against noise and decoherence

Applicable to superconducting artificial atoms

Abstract

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).