Machine-learning based three-qubit gate for realization of a Toffoli gate with cQED-based transmon systems
This work addresses the need for efficient multi-qubit gates in quantum computing, particularly for cQED-based transmon systems, representing an incremental improvement with specific performance gains.
The researchers tackled the problem of designing a high-fidelity three-qubit gate for quantum computing by using machine learning to create a 50 ns controlled-controlled-phase gate with fidelity >99.99% for transmon systems, which enables a Toffoli gate for applications like quantum circuits and error correction.
We use machine learning techniques to design a 50 ns three-qubit flux-tunable controlled-controlled-phase gate with fidelity of >99.99% for nearest-neighbor coupled transmons in circuit quantum electrodynamics architectures. We explain our gate design procedure where we enforce realistic constraints, and analyze the new gate's robustness under decoherence, distortion, and random noise. Our controlled-controlled-phase gate in combination with two single-qubit gates realizes a Toffoli gate which is widely used in quantum circuits, logic synthesis, quantum error correction, and quantum games.