Full-Stack, Real-System Quantum Computer Studies: Architectural Comparisons and Design Insights

• Published in: 2019 ACM/IEEE 46th Annual International Symposium on Computer Architecture (ISCA) pp. 527 - 540
• Main Authors: Murali, Prakash, Linke, Norbert Matthias, Martonosi, Margaret, Abhari, Ali Javadi, Nguyen, Nhung Hong, Alderete, Cinthia Huerta
• Format: Conference Proceeding
• Language: English
• Published: ACM 01.06.2019
• ISSN: 2575-713X
• DOI: 10.1145/3307650.3322273

In recent years, Quantum Computing (QC) has progressed to the point where small working prototypes are available for use. Termed Noisy Intermediate-Scale Quantum (NISQ) computers, these prototypes are too small for large benchmarks or even for Quantum Error Correction (QEC), but they do have sufficient resources to run small benchmarks, particularly if compiled with optimizations to make use of scarce qubits and limited operation counts and coherence times. QC has not yet, however, settled on a particular preferred device implementation technology, and indeed different NISQ prototypes implement qubits with very different physical approaches and therefore widely-varying device and machine characteristics. Our work performs a full-stack, benchmark-driven hardware- software analysis of QC systems. We evaluate QC architectural possibilities, software-visible gates, and software optimizations to tackle fundamental design questions about gate set choices, communication topology, the factors affecting benchmark performance and compiler optimizations. In order to answer key cross-technology and cross-platform design questions, our work has built the first top-to- bottom toolflow to target different qubit device technologies, including superconducting and trapped ion qubits which are the current QC front-runners. We use our toolflow, TriQ, to conduct real-system measurements on seven running QC prototypes from three different groups, IBM, Rigetti, and University of Maryland. Overall, we demonstrate that leveraging microarchitecture details in the compiler improves program success rate up to 28x on IBM (geomean 3x), 2.3x on Rigetti (geomean 1.45x), and 1.47x on UMDTI (geomean 1.17x), compared to vendor toolflows. In addition, from these real-system experiences at QC's hardware-software interface, we make observations and recommendations about native and software-visible gates for different QC technologies, as well as communication topologies, and the value of noise-aware compilation even on lower-noise platforms. This is the largest cross-platform real-system QC study performed thus far; its results have the potential to inform both QC device and compiler design going forward.