English

Quantum self-consistent equation-of-motion method for computing molecular excitation energies, ionization potentials, and electron affinities on a quantum computer

Quantum Physics 2023-02-24 v2 Strongly Correlated Electrons Chemical Physics

Abstract

Near-term quantum computers are expected to facilitate material and chemical research through accurate molecular simulations. Several developments have already shown that accurate ground-state energies for small molecules can be evaluated on present-day quantum devices. Although electronically excited states play a vital role in chemical processes and applications, the search for a reliable and practical approach for routine excited-state calculations on near-term quantum devices is ongoing. Inspired by excited-state methods developed for the unitary coupled-cluster theory in quantum chemistry, we present an equation-of-motion-based method to compute excitation energies following the variational quantum eigensolver algorithm for ground-state calculations on a quantum computer. We perform numerical simulations on H2_2, H4_4, H2_2O, and LiH molecules to test our quantum self-consistent equation-of-motion (q-sc-EOM) method and compare it to other current state-of-the-art methods. q-sc-EOM makes use of self-consistent operators to satisfy the vacuum annihilation condition, a critical property for accurate calculations. It provides real and size-intensive energy differences corresponding to vertical excitation energies, ionization potentials and electron affinities. We also find that q-sc-EOM is more suitable for implementation on NISQ devices as it is expected to be more resilient to noise compared with the currently available methods.

Keywords

Cite

@article{arxiv.2206.10502,
  title  = {Quantum self-consistent equation-of-motion method for computing molecular excitation energies, ionization potentials, and electron affinities on a quantum computer},
  author = {Ayush Asthana and Ashutosh Kumar and Vibin Abraham and Harper Grimsley and Yu Zhang and Lukasz Cincio and Sergei Tretiak and Pavel A. Dub and Sophia E. Economou and Edwin Barnes and Nicholas J. Mayhall},
  journal= {arXiv preprint arXiv:2206.10502},
  year   = {2023}
}