Quantum Chemistry as an application-insprired Benchmark on near-term quantum computers

Near term quantum computing applications can inspire benchmarks and can serve as predictors for future machine performance as the hardware improves. We will present results from application-inspired benchmarks, including Quantum Chemistry. The quantum chemistry benchmark for noisy intermediate-scale quantum computers leverages the variational quantum eigensolver, active space reduction, a modified unitary coupled cluster ansatz, and reduced density purification as error mitigation. We demonstrate this benchmark on the 20 qubit IBM Tokyo and 16 qubit Rigetti Aspen processors via the simulation of alkali metal hydrides (NaH, KH, RbH), with accuracy of the ground state energy as the primary benchmark metric. We also show how to reduce the noise in post processing with specific error mitigation techniques. The adaptation of McWeeny purification of noisy density matrices dramatically improves accuracy of quantum computations, which, along with adjustable active space, significantly extends the range of accessible molecular systems. We demonstrate that for specific benchmark settings, the accuracy metric can reach chemical accuracy when computing over the cloud on certain quantum computers.