Machine-learning based three-qubit gate for realization of a Toffoli gate with cQED-based transmon systems

TL;DR

This paper presents a machine learning-designed three-qubit flux-tunable gate with high fidelity for transmon systems, enabling efficient realization of a Toffoli gate in circuit QED architectures.

Contribution

It introduces a novel machine learning approach to design a fast, high-fidelity three-qubit gate compatible with realistic constraints in transmon-based quantum systems.

Findings

Achieved a 50 ns controlled-controlled-phase gate with >99.99% fidelity.

Demonstrated robustness of the gate under decoherence and noise.

Enabled implementation of a Toffoli gate using the designed three-qubit gate.

Abstract

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.