Near-Term Stepping Stones on the Path to Useful Quantum Computing

Abstract

In 2000, David P. DiVincenzo proposed seven necessary criteria to construct a physical quantum computer. His requirements included device scalability, having well-defined qubits with long coherence times, and the ability to implement a universal gate set. Since then, research has propelled several experimental platforms to the forefront as contenders for quantum computing. The near-term implementations of these various architectures often face technical challenges -- such as limited scalability, short coherence times, or sub-universal computation -- leading to only a partial fulfillment of DiVincenzo's criteria. Despite device shortcomings, progress marches forward with increasingly useful demonstrations of quantum computation and simulation. Towards this end, the research compiled in this thesis presents novel quantum computing methods and applications that can be leveraged in the current era of constrained hardware capabilities. This thesis examines a diverse array of near-term topics, including the use of machine learning to aid in sample-efficient quantum state reconstruction via Born machines, methods to link distributed quantum simulators with incomplete information transfer for approximate fragmented simulation, universal computation with globally controlled analog simulators, and fast scrambling achieved with measurement-only quantum circuits.