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Machine-learning-based three-qubit gate design for the Toffoli gate and parity check in transmon systems

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We use machine-learning techniques to design three-qubit entangling gates with fidelities of g99.9% and duration of 50 ns for nearest-neighbor coupled flux-tunable transmons in circuit quantum electrodynamics architectures. The gate… Click to show full abstract

We use machine-learning techniques to design three-qubit entangling gates with fidelities of g99.9% and duration of 50 ns for nearest-neighbor coupled flux-tunable transmons in circuit quantum electrodynamics architectures. The gate design procedure enforces realistic constraints and analyzes the robustness of the new gates under decoherence, distortion, and random noise. The controlled-controlled-phase gate in combination with two single-qubit gates realizes a Toffoli gate which is widely used in quantum circuits, logic synthesis, and quantum error correction. We also introduce a three-qubit entangling Parity Checker gate which has applications in quantum arithmetic circuits and quantum error correction schemes. Using these three-qubit gates, we design a circuit for Shor's nine-qubit quantum error correction code and compare its performance to conventional realizations.

Keywords: machine learning; gate; three qubit; gate design; toffoli gate

Journal Title: Physical Review A
Year Published: 2020

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