| Summary: | Quantum computing has the potential to solve problems that take current classical computers much longer to solve. The last two decades witnessed a substantial development of various physical platforms, most of which have shown a "Proof of principle" demonstration for quantum computing. We are now in a critical stage where the goal is to develop the system to a large scale so that we can make quantum computing useful for real world applications. However, the question of which physical platform shows most promise remains uncertain. In this thesis, I examine two mainstream platforms: linear optics and trapped ions, from the point of view of scalability. For each platform, my own work has focused particularly on how to model and engineer the temporal properties of single photons. For linear optical quantum computing, I propose to use an off-resonant cascaded absorption (ORCA) buffer to purify and unify the single-photons from different noisy sources. A proof of principle experiment is demonstrated to show that the ORCA buffer is in principle, noise-free. Simulation suggests that with the current ORCA buffer, we can purify the single-photon source better than with ideal intensity filtering. Meanwhile, it can unify different sources and increase the inter-indistinguishability from 60% to 96%. For the trapped ion system, one promising scalable approach is to use photons to mediate entanglement between ions in remote traps. To achieve this, ion-photon entanglement must first be generated. We propose to use a cavity-enhanced Raman transition on the D3/2 → P3/2 → D5/2 λ-system' in a strontium ion level scheme for ion-photon entanglement generation. A full mathematical description is constructed to describe the entanglement dynamics considering the Rayleigh-scattering-induced temporal mixing noise. With this theory, simulation shows that we expect to achieve ion-ion remote entanglement rate of more than 75KHz with negligible infidelity from temporal mixing noise. Both proposals, if fully demonstrated experimentally, will ...
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