标题： 量子计算在基因组学中的应用：概念性挑战与实际视角 显示英文标题

标题： Quantum computing for genomics: conceptual challenges and practical perspectives

摘要： 我们评估量子计算在加速基因组学中核心任务计算方面的潜力，重点关注常被忽视的理论限制。 我们讨论量子搜索、优化和机器学习算法的最新挑战。 通过研究使用Grover算法的数据库搜索，我们表明在现实假设下预期的加速效果会消失。 对于基因组学中常见的组合优化问题，我们讨论了理论复杂性在实践中的局限性，并建议仔细识别真正适合量子加速的问题。 鉴于优秀经典近似求解器的竞争，量子计算只有在组装、基因选择和推断中特定的足够困难任务子集中才可能在未来带来加速。 这些任务需要由对经典方法特别具有挑战性的核心优化问题组成，同时需要相对有限的变量。 我们强调通过运行时间缩放分析进行严格的实证验证，以避免对量子优势的误导性声明。 最后，我们讨论了量子机器学习中的可训练性和数据加载问题。 这项工作倡导在基因组学中对量子计算采取平衡的观点，引导未来的研究走向针对性的应用和稳健的验证。 显示英文摘要

摘要： We assess the potential of quantum computing to accelerate computation of central tasks in genomics, focusing on often-neglected theoretical limitations. We discuss state-of-the-art challenges of quantum search, optimization, and machine learning algorithms. Examining database search with Grover's algorithm, we show that the expected speedup vanishes under realistic assumptions. For combinatorial optimization prevalent in genomics, we discuss the limitations of theoretical complexity in practice and suggest carefully identifying problems genuinely suited for quantum acceleration. Given the competition from excellent classical approximate solvers, quantum computing could offer a speedup in the near future only for a specific subset of hard enough tasks in assembly, gene selection, and inference. These tasks need to be characterized by core optimization problems that are particularly challenging for classical methods while requiring relatively limited variables. We emphasize rigorous empirical validation through runtime scaling analysis to avoid misleading claims of quantum advantage. Finally, we discuss the problem of trainability and data-loading in quantum machine learning. This work advocates for a balanced perspective on quantum computing in genomics, guiding future research toward targeted applications and robust validation.