Designing High-Fidelity Single-Shot Three-Qubit Gates: A Machine Learning Approach
This work addresses the challenge of reliable quantum gate design for quantum error correction and information processing, representing an incremental improvement with specific application to superconducting systems.
The researchers tackled the problem of designing high-fidelity three-qubit gates for quantum computing by using a machine learning approach, achieving 99.9% fidelity in simulations for Toffoli, Controlled-Not-Not, and Fredkin gates, which meets fault-tolerant thresholds.
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).