Abstract
We present a quantum chemistry benchmark for noisy intermediate-scale quantum computers that leverages the variational quantum eigensolver, active-space reduction, a reduced unitary coupled cluster ansatz, and reduced density purification as error mitigation. We demonstrate this benchmark using 4 of the available qubits 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 computed ground state energy serving as the primary benchmark metric. We further parameterize this benchmark suite on the trial circuit type, the level of symmetry reduction, and error mitigation strategies. Our results demonstrate the characteristically high noise level present in near-term superconducting hardware, but provide a relevant baseline for future improvement of the underlying hardware, and a means for comparison across near-term hardware types. We also demonstrate how to reduce the noise in post processing with specific error mitigation techniques. Particularly, 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 and a selected range of problems, the accuracy metric can reach chemical accuracy when computing over the cloud on certain quantum computers.
Highlights
Noisy intermediate-scale quantum (NISQ) devices have been used recently to demonstrate a variety of different small-scale quantum computations.[1,2,3,4,5] These demonstrations underscore the progress in developing programmable quantum processing units (QPUs) as well as advances in how to use these devices for scientifically meaningful computations
We computed the energy at the optimal parameter twice and performed error mitigation with an increasing number of noisy CNOT identity pairs (r = 1 is the original circuit, r = 3 has each CNOT replaced with 3 CNOTs; see Methods section)
We found that the hwe ansatz was unable to produce results comparable to energies calculated using a classical, full configuration interaction (FCI) method
Summary
Noisy intermediate-scale quantum (NISQ) devices have been used recently to demonstrate a variety of different small-scale quantum computations.[1,2,3,4,5] These demonstrations underscore the progress in developing programmable quantum processing units (QPUs) as well as advances in how to use these devices for scientifically meaningful computations. Several properties quantify the intrinsic utility of a QPU including the capacity of the quantum register, the fidelity of the available instructions, the connectivity between register elements, etc. While these hardware-specific parameters characterize progress in quantum hardware development, they do not directly benchmark the performance of a quantum computer for a given computational task. Benchmarks for application-specific metrics are needed to evaluate the efficiency and applicability of quantum computing for scientific applications. Application-specific performance metrics have been used recently to evaluate the utility of near-term QPUs with respect to computational accuracy, including examples from machine learning[6] and nuclear physics.[4] the design and demonstration of an application-specific benchmark to evaluate QPU performance across scalable problem instances has been absent from the literature up to now
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