Considerations for evaluating thermodynamic properties with hybrid quantum-classical computing work flows

Quantum chemistry applications on quantum computers currently rely heavily on the variational quantum eigensolver (VQE) algorithm. This hybrid quantum-classical algorithm aims at finding ground-state solutions of molecular systems based on the variational principle. VQE calculations can be systematically implemented for perturbations to each molecular degree of freedom, generating a Born-Oppenheimer potential-energy surface (PES) for the molecule. The PES can then be used to derive thermodynamic properties, which are often desirable for applications in chemical engineering and materials design. It is clear from this process that quantum chemistry applications contain a substantial classical computing component in addition to steps that can be performed using a quantum computer. In order to design efficient work flows that take full advantage of each hardware type, it is critical to consider the entire process so that the high-accuracy electronic energies possible from quantum computing are not squandered in the process of calculating thermodynamic properties. We present a summary of the hybrid quantum-classical work flow to compute thermodynamic properties. This work flow contains many options that can significantly affect the efficiency and the accuracy of the results, including classical optimizer attributes, number of ansatz repetitions, and how the vibrational Schrodinger equation is solved to determine vibrational modes. We also analyze the effects of these options by employing robust statistics along with simulations and experiments on actual quantum hardware. We show that, through careful selection of work flow options, nearly order-of-magnitude increases in accuracy are possible at equivalent computing time.

Automated summary

Quantum chemistry applications rely on the VQE algorithm to compute thermodynamic properties. Designing efficient workflows that utilize both quantum and classical hardware is crucial. By carefully selecting workflow options, significant improvements in accuracy can be achieved.