用于DNA组装的量子反馈算法：FALQON变体
Quantum feedback algorithms for DNA assembly using FALQON variants
AI总结：
本文提出三种FALQON变体用于DNA从头组装，通过量子反馈算法提升基态收敛速度和成功概率。
摘要：
无参考序列重建DNA序列，即从头组装，是一个复杂的计算任务，涉及重叠片段的对齐。为解决这个问题，通常策略是将组装映射到二次无约束二元优化（QUBO）公式，这可以通过不同的量子算法求解。在本工作中，我们关注三种反馈算法的变体，这是一种通过测量反馈消除经典优化循环的协议。我们使用标准FALQON、二级FALQON（SO-FALQON）和时间重新标定FALQON（TR-FALQON）分析长读DNA片段和SARS-CoV-2和人类线粒体DNA。数值结果表明，这两种变体提高了收敛到基态的速度，并在减少电路深度的情况下提高了成功概率。这些发现表明，增强的反馈驱动动力学在近期量子硬件上解决组合问题方面是有效的。
英文摘要：
Reconstructing DNA sequences without a reference, known as de novo assembly, is a complex computational task involving the alignment of overlapping fragments. To address this problem, a usual strategy is to map the assembly to a Quadratic Unconstrained Binary Optimization (QUBO) formulation, which can be solved by different quantum algorithms. In this work, we focus on three versions of the Feedback-based Algorithm, a protocol that eliminates classical optimization loops via measurement feedback. We analyze long-read DNA fragments from SARS-CoV-2 and human mitochondrial DNA using standard FALQON, second-order FALQON (SO-FALQON), and time-rescaled FALQON (TR-FALQON). Numerical results show that both variants improve convergence to the ground state and increase success probabilities at reduced circuit depths. These findings indicate that enhanced feedback-driven dynamics are effective for solving combinatorial problems on near-term quantum hardware.
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