Design, Optimization, and Simulation of Scalable Quantum Computing Systems

Quantum computing (QC) aims to solve certain computational problems beyond the capabilities of even the largest classical high-performance computers. By leveraging the quantum mechanical principles of superposition and entanglement, QC algorithms have the potential to revolutionize areas such as machine learning, quantum chemistry, and cryptography. To perform quantum advantages, a scalable quantum computing system is essential. This thesis demonstrates techniques that address problems of the development of scalable quantum computers. The first part of the thesis describes our technique to perform scalable quantum circuit optimization by using synthesis. Traditional quantum synthesis can only be used for circuits on a handful of qubits. The proposed technique partitions the circuit into multiple small pieces of circuits and performs quantum synthesis individually. This technique enables quantum circuit synthesis for large circuits. The second section is the design and evaluation of the trapped-ion linear-tape architecture. This study provides an alternative idea towards scalable quantum computing systems. The last section of the thesis is our technique to increase the capability of quantum circuit simulation on classical supercomputing systems. Our hybrid data compression technique is introduced into the simulation of quantum computing. This work allows us to further push the limit of classical simulation, and hence verify a larger size of quantum computing systems.