Connor Hann

Quantum computers are expected to revolutionize the world of computing, but major challenges remain to be addressed before this potential can be realized. One such challenge is the so-called data-input bottleneck: Even though quantum computers can quickly solve certain problems by rapidly analyzing large data sets, it can be difficult to load this data into a quantum computer in the first place. In order to quickly load large data sets into quantum states, a highly-specialized device called a Quantum Random Access Memory (QRAM) is required. Building a large-scale QRAM is a daunting engineering challenge, however, and concerns about QRAM’s practicality cast doubt on many potential quantum computing applications.

In this thesis, I consider the practical challenges associated with constructing a large-scale QRAM and describe how several of these challenges can be addressed. I first show that QRAM is surprisingly resilient to decoherence, such that data can be reliably loaded even in the presence of realistic noise. Then, I propose a hardware-efficient error suppression scheme that can further improve QRAM’s reliability without incurring substantial additional overhead, in contrast to conventional quantum error-correction approaches. Finally, I propose experimental implementations of QRAM for hybrid quantum acoustic systems. The proposed architectures are naturally hardware-efficient and scalable, thanks to the compactness and high coherence of acoustic modes. Taken together, the results in this thesis both pave the way for small-scale, near-term experimental demonstrations of QRAM and improve the reliability and scalability of QRAM in the long term.