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Estimation of Electrostatic Interaction Energies on a Trapped-Ion Quantum Computer.

Pauline J OllitraultMatthias LoipersbergerRobert M ParrishAlexander ErhardChristine MaierChristian SommerJuris UlmanisThomas MonzChristian GogolinChristofer S TautermannGian-Luca R AnselmettiMatthias DegrooteNikolaj MollRaffaele SantagatiMichael Streif
Published in: ACS central science (2024)
We present the first hardware implementation of electrostatic interaction energies by using a trapped-ion quantum computer. As test system for our computation, we focus on the reduction of NO to N 2 O catalyzed by a nitric oxide reductase (NOR). The quantum computer is used to generate an approximate ground state within the NOR active space. To efficiently measure the necessary one-particle density matrices, we incorporate fermionic basis rotations into the quantum circuit without extending the circuit length, laying the groundwork for further efficient measurement routines using factorizations. Measurements in the computational basis are then used as inputs for computing the electrostatic interaction energies on a classical computer. Our experimental results strongly agree with classical noise-less simulations of the same circuits, finding electrostatic interaction energies within chemical accuracy despite hardware noise. This work shows that algorithms tailored to specific observables of interest, such as interaction energies, may require significantly fewer quantum resources than individual ground state energies would require in the straightforward supermolecular approach.
Keyphrases
  • molecular dynamics
  • density functional theory
  • deep learning
  • nitric oxide
  • monte carlo
  • molecular dynamics simulations
  • healthcare
  • machine learning
  • air pollution
  • energy transfer
  • primary care