Topics in Quantum Computing Architecture

Abstract

Quantum computing, which is considered the next revolution of computing technology, brings together theories of mathematics, physics, and computer science. Building a quantum computer thus requires a synthesis of knowledge and skills from multiple disciplines. In this thesis, we take a step toward bridging and connecting the full "stack" of quantum computing technology which spans across theoretical foundations, hardware architecture, software and simulation.

At the foundation level, we study one of the central problems of quantum computing, namely quantum error correction, from a control-engineering perspective. This approach not only complements the conventional coding-based interpretation but also provides a potential pathway to designing self-correcting quantum computers. First, we analyse the surface-code quantum error correction under a continuous feedback-based protocol. Second, we study the fundamental question of self-correcting quantum systems using the control method of reservoir engineering.

Next, we study the scalability of a generic surface code quantum computer based on spin qubits such as quantum dots and donor atoms. Solid-state qubits (quantum dots and donor atoms), especially those that are Si-based, share similarities in device structures and manufacturing processes with advanced semiconductor CMOS industry. However, scaling up those quantum devices present immense challenges related to connectivity. By applying tools and methods from the semiconductor industry, we can concretely estimate the routing limitation of a planar connectivity scheme.

Finally, as part of my PhD education at the Australian National University, I took part in an industry-based research internship to develop a high-performance quantum simulator. We provide the full description of the software architecture, implementation details, and benchmarking results of the simulator.