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Quantum Computation of Electronic Transitions Using a Variational Quantum Eigensolver.

Robert M Parrish1,2,3, Edward G Hohenstein1,2, Peter L McMahon3,4

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We introduce a new quantum algorithm, multistate contracted VQE (MC-VQE), for efficiently calculating molecular transition energies and oscillator strengths. This method accurately simulates the absorption spectrum of complex light-harvesting systems.

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Area of Science:

  • Quantum computing
  • Computational chemistry
  • Spectroscopy

Background:

  • The variational quantum eigensolver (VQE) is a prominent algorithm for quantum chemistry simulations.
  • Calculating excited states and transition properties of molecules is computationally demanding.
  • Accurate simulation of molecular spectra is crucial for understanding light-harvesting complexes.

Purpose of the Study:

  • To develop an efficient quantum algorithm for computing molecular transition energies and oscillator strengths.
  • To extend the capabilities of the variational quantum eigensolver (VQE) for excited-state calculations.
  • To simulate the absorption spectrum of a complex biological system using the new algorithm.

Main Methods:

  • Development of the multistate contracted VQE (MC-VQE) algorithm.
  • Numerical simulation of MC-VQE.
  • Computation of the absorption spectrum for an ab initio exciton model.
  • Application to an 18-chromophore light-harvesting complex from purple photosynthetic bacteria.

Main Results:

  • The MC-VQE algorithm enables efficient computation of transition energies between ground and low-lying excited states.
  • Oscillator strengths associated with these transitions can be accurately determined.
  • Numerical simulations demonstrate the method's efficacy on a complex light-harvesting system.

Conclusions:

  • MC-VQE offers a significant advancement for quantum computation of molecular excited-state properties.
  • The method provides a pathway for accurate spectral simulations of complex molecular systems.
  • This work highlights the potential of quantum algorithms in understanding biological light-harvesting processes.