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

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.

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• Fast multi-qubit gates through simultaneous two-qubit gates
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• Switchable next-nearest-neighbor coupling for controlled two-qubit operations
• Direct Implementation of High-Fidelity Three-Qubit Gates for Superconducting Processor with Tunable Couplers
• Approximations in transmon simulation
• Designing high-fidelity multi-qubit gates for semiconductor quantum dots through deep reinforcement learning
• Machine-learning-inspired quantum optimal control of nonadiabatic geometric quantum computation via reverse engineering