UC Santa Barbara

With the rapid advancements in quantum hardware, we stand on the brink of the large-scale quantum computing (LSQC) era, poised to harness the power of thousands of even millions of noisy qubits. This heralds a transformative period where the computational prowess of LSQC holds promises for addressing practical scientific challenges, notably in the realms of molecule simulation and drug discovery. However, despite this exciting progress, a noticeable gap persists between the current Noisy Intermediate-Scale Quantum (NISQ)-era ecosystem and the full potential of LSQC. On the one hand, the scale of LSQC makes manual hardware/software optimizations untenable. On the other hand, the noisy nature of quantum hardware, accompanying the instruction scale of large quantum applications, will surely destroy the quantum computing outcome. These aspects of LSQC induces a series of challenges and requirements not well optimized and supported by NISQ computing stack: automation, scalability, and robustness.

The dissertation aims to fill this gap and synthesize a full-stack design for the LSQC computing stack. The proposed framework presents systematic optimizations for quantum software and quantum architecture. The vision is that significant advances in LSQC require full-stack infrastructure that could not only vertically integrate the advances from both the higher problem level and the lower hardware level. Concerning rapidly evolving problem size and hardware magnitude, the value of the proposed design will only increase. It provides a clearer road-map for building the LSQC in the near future. The evaluation demonstrates the superiority of the proposed framework.