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This study demonstrates the first quantum simulation of conical intersections in cytosine using a superconducting quantum computer. These simulations are crucial for understanding DNA photostability and biological processes.

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Area of Science:

  • Quantum Computing
  • Computational Chemistry
  • Molecular Biology

Background:

  • Hybrid quantum-classical algorithms can accelerate electronic structure calculations for complex molecules.
  • Conical intersections (CIs) are vital for understanding the photostability of DNA and RNA.
  • Simulating CIs is essential for advancing knowledge in photochemistry and photobiology.

Purpose of the Study:

  • To perform the first quantum simulation of conical intersections (CIs) in a biomolecule, cytosine.
  • To compute the electronic structure of near-degenerate ground and excited states at CIs.
  • To evaluate the performance of the contracted quantum eigensolver (CQE) and variational quantum deflation (VQD) algorithms on quantum hardware.

Main Methods:

  • Utilized a superconducting quantum computer for quantum simulations.
  • Applied the contracted quantum eigensolver (CQE) algorithm, known for its exact ansatz.
  • Compared CQE performance with conventional variational quantum deflation (VQD) and exact diagonalization.

Main Results:

  • Successfully simulated conical intersections in the biomolecule cytosine.
  • Both CQE and VQD showed promising accuracy compared to exact diagonalization.
  • Demonstrated the potential of quantum algorithms on noisy intermediate-scale quantum computers for CI studies.

Conclusions:

  • Quantum simulations of CIs are feasible and accurate, even on current noisy quantum hardware.
  • This work advances the understanding of photochemical processes in biomolecules.
  • Potential implications for molecular biology, DNA repair, mutation studies, and medical research.