Benchmarking simulated and physical quantum processing units using quantum and hybrid algorithms
It provides a large-scale performance comparison to guide optimal hardware-software selection for practical quantum algorithms, though it is incremental as it benchmarks existing platforms without introducing new methods.
This work benchmarks runtime and accuracy across simulated and physical quantum processing units, finding that QMware simulator reduces runtime by up to 78% for circuits under 27 qubits, while physical devices like Rigetti's Aspen-M2 offer exponential runtime advantages for over 30 qubits but face high costs and fidelity issues.
Powerful hardware services and software libraries are vital tools for quickly and affordably designing, testing, and executing quantum algorithms. A robust large-scale study of how the performance of these platforms scales with the number of qubits is key to providing quantum solutions to challenging industry problems. This work benchmarks the runtime and accuracy for a representative sample of specialized high-performance simulated and physical quantum processing units. Results show the QMware simulator can reduce the runtime for executing a quantum circuit by up to 78% compared to the next fastest option for algorithms with fewer than 27 qubits. The AWS SV1 simulator offers a runtime advantage for larger circuits, up to the maximum 34 qubits available with SV1. Beyond this limit, QMware can execute circuits as large as 40 qubits. Physical quantum devices, such as Rigetti's Aspen-M2, can provide an exponential runtime advantage for circuits with more than 30 qubits. However, the high financial cost of physical quantum processing units presents a serious barrier to practical use. Moreover, only IonQ's Harmony quantum device achieves high fidelity with more than four qubits. This study paves the way to understanding the optimal combination of available software and hardware for executing practical quantum algorithms.