Application-Motivated, Holistic Benchmarking of a Full Quantum Computing Stack

Understand

Quantum computing systems need to be benchmarked in terms of practical tasks they would be expected to do.

• Here, we propose 3 "application-motivated" circuit classes for benchmarking: deep (relevant for state preparation in the variational quantum eigensolver algorithm), shallow (inspired by IQP-type circuits that might be useful for near-term quantum machine learning), and square (inspired by the quantum volume benchmark).
• We quantify the performance of a quantum computing system in running circuits from these classes using several figures of merit, all of which require exponential classical computing resources and a polynomial number of classical samples (bitstrings) from the system.
• We study how performance varies with the compilation strategy used and the device on which the circuit is run.