A Gate-Based Quantum Computing Framework for Codon Optimization

@misc{ghasemi2026gatebased,
  title = {{A Gate-Based Quantum Computing Framework for Codon Optimization}},
  author = {Fatemeh Ghasemi and Kion Kim},
  howpublished = {arXiv},
  year = {2026},
  month = oct,
  doi = {10.48550/arxiv.2610.00887},
  eprint = {2610.00887},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2610.00887},
}

TY  - UNPB
TI  - A Gate-Based Quantum Computing Framework for Codon Optimization
AU  - Fatemeh Ghasemi
AU  - Kion Kim
PY  - 2026
DA  - 2026/10/01
PB  - arXiv
DO  - 10.48550/arxiv.2610.00887
UR  - https://arxiv.org/abs/2610.00887
AB  - Codon optimization is a challenging combinatorial optimization problem with important applications in synthetic biology, protein expression, and biotechnology. While quantum annealing has previously been explored for this problem, the application of gate-based quantum algorithms remains largely unexplored. In this work, we present a gate-based quantum computing framework for codon optimization by formulating the optimization objective as an Ising Hamiltonian and expressing it in terms of Pauli operators suitable for gate-based quantum processors. The proposed framework is investigated using the Variational Quantum Eigensolver (VQE), Sampling Variational Quantum Eigensolver (SVQE), and the Quantum Approximate Optimization Algorithm (QAOA), with benchmarking against exact diagonalization, classical optimization, and quantum annealing. Using a three-amino-acid benchmark, the proposed implementations successfully reproduce the exact ground-state solutions while identifying optimized ansatz selections, optimization strategies, initialization schemes, and hardware execution settings that provide reproducible performance on current noisy quantum hardware. Hardware demonstrations are performed on IBM Quantum processors to evaluate the practical implementation of the proposed workflow. As a work in progress, this study establishes a reproducible computational framework for gate-based quantum codon optimization and provides the foundation for future investigations of larger protein sequences as quantum hardware and algorithms continue to advance.
ER  -