发表机构
Yonsei University International Campus（延世大学国际校区）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出基于门控量子计算的密码子优化框架，利用VQE、SVQE和QAOA算法，在三氨基酸基准上成功复现精确基态解，并在IBM量子硬件上验证了可复现性能，为未来更大蛋白质序列的量子优化奠定基础。
AI 中文摘要
密码子优化是一个具有挑战性的组合优化问题，在合成生物学、蛋白质表达和生物技术中具有重要应用。虽然量子退火此前已被探索用于该问题，但基于门控的量子算法的应用在很大程度上仍未得到探索。在本工作中，我们提出了一种基于门控量子计算的密码子优化框架，通过将优化目标表述为伊辛哈密顿量，并将其表示为适用于门控量子处理器的泡利算符。所提出的框架使用变分量子本征求解器（VQE）、采样变分量子本征求解器（SVQE）和量子近似优化算法（QAOA）进行研究，并与精确对角化、经典优化和量子退火进行基准比较。使用三氨基酸基准测试，所提出的实现成功重现了精确基态解，同时确定了优化的拟设选择、优化策略、初始化方案和硬件执行设置，这些设置在当前的噪声量子硬件上提供了可复现的性能。硬件演示在IBM量子处理器上执行，以评估所提出工作流的实际实现。作为一项正在进行的工作，本研究为基于门控的量子密码子优化建立了一个可复现的计算框架，并为未来随着量子硬件和算法不断进步而研究更大蛋白质序列提供了基础。
英文摘要
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.
