Improving Scalability of Trapped-Ion Quantum Computers Using Gate-Level Techniques

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Date

2023

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Abstract

Trapped ions provide a promising platform to build a practical quantum computer. Scaling the high performance of small systems to longer ion chains is a technical endeavor that benefits from both better hardware system design and gate-level control techniques. In this thesis, I discuss our work on building a small-scale trapped-ion quantum computing system that features stable laser beam control, low-crosstalk individual addressing and capability to implement high-fidelity multi-qubit gates.

We develop control techniques to extend the pack-leading fidelity of entangling gates in two-ion systems to longer chains. A major error source limiting entangling gate fidelities in ion chains is crosstalk between target and neighboring spectator qubits. We propose and demonstrate a crosstalk suppression scheme that eliminates all first-order crosstalk utilizing only local control of target qubits, as opposed to an existing scheme which requires control over all neighboring qubits. Using the scheme, we achieve a $99.5\%$ gate fidelity in a 5-ion chain. Complex quantum circuits can benefit from native multi-qubit gates such as the $N$-Toffoli gate, which substantially reduce the overhead cost from performing universal decomposition into single- and two-qubit gates. We take advantage of novel performance benefits of long ion chains to realize scalable Cirac-Zoller gates, which uses a simple pulse sequence to efficiently implement $N$-Toffoli gates. We demonstrate the Cirac-Zoller 3- and 4-Toffoli gates in a five-ion chain with higher fidelities than previous results using trapped ions. We also present the first experimental realization of a 5-Toffoli gate.

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Quantum physics, Atomic physics, Applied physics, atomic molecular and optical physics, ion trap, Quantum computing, quantum control, quantum gates, Quantum information

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Citation

Fang, Chao (2023). Improving Scalability of Trapped-Ion Quantum Computers Using Gate-Level Techniques. Dissertation, Duke University. Retrieved from https://hdl.handle.net/10161/27742.

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