Quantum Graph Analysis

In recent years, advanced network analytics have become increasingly important to national security with applications ranging from cyber security to detection and disruption of terrorist networks. While classical computing solutions have received considerable investment, the development of quantum algorithms to address problems, such as data mining of attributed relational graphs, is a largely unexplored space. Recent theoretical work has shown that quantum algorithms for graph analysis can be more efficient than their classical counterparts. Here, we have implemented a trapped-ion-based two-qubit quantum information processor to address these goals. Building on Sandia’s microfabricated silicon surface ion traps, we have designed, realized and characterized a quantum information processor using the hyperfine qubits encoded in two 171Yb+ ions. We have implemented single qubit gates using resonant microwave radiation and have employed Gate set tomography (GST) to characterize the quantum process. For the first time, we were able to prove that the quantum process surpasses the fault tolerance thresholds of some quantum codes by demonstrating a diamond norm distance of less than 1.9×10−4. We used Raman transitions in order to manipulate the trapped ions’ motion and realize two-qubit gates. We characterized the implemented motion sensitive and insensitive single qubit processes and achieved a maximal process infidelity of 6.5×10−5. We implemented the two-qubit gate proposed by Mølmer and Sørensen and achieved a fidelity of more than 97.7%.