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

Area of Science:

• Quantum computing
• Computational chemistry
• Biocatalysis

Background:

• Nitric oxide reductase (NOR) plays a crucial role in biological denitrification.
• Accurate computation of electrostatic interaction energies is vital for understanding enzyme mechanisms.
• Trapped-ion quantum computers offer a novel platform for molecular simulations.

Purpose of the Study:

• To implement and demonstrate the hardware calculation of electrostatic interaction energies on a trapped-ion quantum computer.
• To apply this method to the reduction of NO to N2O catalyzed by NOR.
• To assess the accuracy and resource requirements of the quantum approach compared to classical methods.

Main Methods:

• Utilized a trapped-ion quantum computer to generate an approximate ground state within the NOR active space.
• Incorporated fermionic basis rotations into the quantum circuit for efficient measurement of one-particle density matrices.
• Performed measurements in the computational basis and used these as inputs for classical computation of electrostatic interaction energies.

Main Results:

• Achieved accurate electrostatic interaction energies within chemical accuracy, comparable to classical simulations.
• Demonstrated the feasibility of hardware implementation of electrostatic energy calculations on a quantum computer.
• Showed that tailored algorithms require fewer quantum resources than direct ground state energy calculations.

Conclusions:

• The first hardware implementation of electrostatic interaction energies using a trapped-ion quantum computer is successful.
• This approach provides accurate results even with hardware noise, validating its potential.
• Tailored quantum algorithms for specific observables like interaction energies are resource-efficient for complex systems.