Complete Methods Set for Scalable Ion Trap Quanum Information Processing

Published: September 04, 2009

Author(s)

Jonathan Home, David Hanneke, John D. Jost, Jason Amini, Dietrich G. Leibfried, David J. Wineland

Abstract

Building a quantum information processor capable of outperforming classical devices will require many quantum bits (qubits) and very large numbers of logical operations \cite{05Knill}. A key requirement is the faithful transport of qubits throughout the processor, which must be performed while retaining both coherence of the qubits and the ability to perform subsequent quantum logic gate with high fidelity \cite{07Steane}. This poses a formidable challenge for all candidate quantum information technologies. For quantum information processing using trapped atomic ions, the ability to combine transport of information with multiple gate operations has been limited until now by heating of the motion (degrading the performance of multi-qubit gates \cite{00Sorensen1, 04Barrett}) and the loss of quantum information due to interaction of qubit states with the (noisy) magnetic field environment \cite{08Blatt}. Here, we demonstrate repetitive two-qubit quantum logic gates combined with information transport and single-qubit gates, where transporting the qubits does not affect subsequent gate performance. We use a hybrid approach, storing qubits in a magnetic field insensitive state manifold \cite{05Langer} for transport and memory, and mapping into other state manifolds for two-qubit gates and detection. We re-initialise motional states prior to each two-qubit gate by cooling ``refrigerant'' ions \cite{03Barrett} that are trapped and transported along with the qubit ions. The combination of repeated transport, multi-qubit logic, and individual addressing for gates and readout demonstrates all of the basic operations required for large scale quantum information processing.
Citation: Science Magazine
Volume: 325
Pub Type: Journals

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Keywords

hybrid qubit storage, scalable, quantum information processing
Created September 04, 2009, Updated February 19, 2017