Scalable Benchmarking of Quantum Chemistry Algorithms using Circuit Mirroring

Current benchmarking methods for measuring progress towards applications on quantum computers often lack scalability and specificity. This limits their ability to produce generalizable metrics of processor performance. We will describe a method for creating scalable application-specific benchmarks using mirrored subcircuits that quantify a device's ability to execute particular subroutines or entire algorithms. We will then apply this method to two quantum chemistry subroutines, performing noisy numerical simulations and interpreting their results within the paradigm of volumetric benchmarking. We will show how to extract effective error rates and predict full circuit fidelities from the subcircuit data and demonstrate that we can distinguish differences in performance resulting from structural properties of the circuits.