Dissertation Committee Chair: Steven Rolston
Committee: Norbert M. Linke, Avik Dutt, Yu Liu, Christopher Jarzynski
Abstract: Quantum computing holds vast potential for solving classically hard problems. While large-scale fault-tolerant quantum computers capable of these tasks are still in the future, small and noisy prototypes have been demonstrated on several candidate platforms. Among these, trapped ion qubits have been at the forefront of quantum computing because of their long coherence times, high-fidelity quantum gates, and all-to-all connectivity. This dissertation investigates efficient ways to utilize quantum resources on a trapped ion quantum computers (TIQC) at the interface between theory and experiment.
The Quantum Approximate Optimization Algorithm (QAOA) can solve graph combinatorial optimization problems by applying multiple rounds of variational circuits. We experimentally show that QAOA results improve with deeper circuits for multiple problems on several arbitrary graphs on a TIQC. We also demonstrate QAOA with a novel mixer which allows fair sampling of all optimal solutions in weighted problems.
We propose and demonstrate a high-fidelity and resource-efficient scheme for driving simultaneous entangling gates in trapped-ion chains. We show the advantage of parallel operation with a simple digital quantum simulation where parallel gates improves the overall fidelity significantly.
Accurate knowledge about quantum noise in real devices can inform hardware design as well as enable the co-design of tailored quantum error correction codes. Noise characterization is a challenging task due to the computational cost. We test an ancilla-assisted Pauli noise learning protocol that uses a sample size linear to the system size. We also design and demonstrate a method to improve its performance by reducing ancilla noise in post-processing.