使用量子和量子启发退火的基因组组装
Genome assembly using quantum and quantum-inspired annealing
摘要：
DNA测序的最新进展为全基因组分析的快速和可靠开辟了前景，这对于包括个性化医疗在内的各种应用都有希望。然而，用于{ \it de novo}基因组组装的现有技术需要解决高计算复杂度的任务，所述基因组组装用于分析基因组重排、染色体定相和在没有参考的情况下重建基因组。在这里，我们展示了一种使用量子和量子启发的优化技术解决基因组组装任务的方法。在这种方法中，我们目前的实验结果基因组组装使用量子退火模拟数据和$ϕ$ X 174噬菌体。我们的研究结果铺平了一条道路，在解决生物信息学问题的效率与使用量子计算，特别是量子退火。我们期望新一代的量子退火设备将优于现有的{ \it de novo}基因组组装技术。据我们所知，这是第一次对量子退火设备和量子启发技术上的真实和合成数据进行从头基因组组装问题的实验研究。
英文摘要：
Recent advances in DNA sequencing open prospects to make whole-genome analysis rapid and reliable, which is promising for various applications including personalized medicine. However, existing techniques for {\it de novo} genome assembly, which is used for the analysis of genomic rearrangements, chromosome phasing, and reconstructing genomes without a reference, require solving tasks of high computational complexity. Here we demonstrate a method for solving genome assembly tasks with the use of quantum and quantum-inspired optimization techniques. Within this method, we present experimental results on genome assembly using quantum annealers both for simulated data and the $ϕ$X 174 bacteriophage. Our results pave a way for an increase in the efficiency of solving bioinformatics problems with the use of quantum computing and, in particular, quantum annealing. We expect that the new generation of quantum annealing devices would outperform existing techniques for {\it de novo} genome assembly. To the best of our knowledge, this is the first experimental study of de novo genome assembly problems both for real and synthetic data on quantum annealing devices and quantum-inspired techniques.
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