Benchmarking and Analysis of Noisy Intermediate-Scale Trapped Ion Quantum Computing Architectures

Quantum computing is at the cusp of showing relevance for real-world problems. However, currently available devices are small in scale, and hardware demonstrations have shown limited applicability to scientific and commercial problems of interest. In this work, we investigate a set of application-specific noisy intermediate-scale quantum (NISQ) algorithms varying in size from 4-80 qubits, and use these benchmarks to evaluate trade-offs in the design of trapped ion quantum computing architectures. We have developed a tool-chain consisting of architecture specific compilation and simulation tools and used these tools to target five different candidate trapped ion quantum computing architectures. Using our tool-chain, we can estimate metrics such as application run-times and overhead. Additionally, we use our tools to determine the critical operations of an architecture and to study the sensitivity to architectural constraints in particular implementations. Our tools are designed to be flexible, allowing us to study a broad range of benchmarks, hardware architectures, physical constraints, and operation timing.