Hybrid Quantum Transformer Architectures in Genomics

变形器和大型语言模型在处理基于语言的信息方面显示出巨大的潜力。它们在连接大型复杂数据集方面的能力使它们优于传统的神经网络。最近有研究表明，变形器（b谷歌的BERT模型）也可以应用于基因组学数据集（DNA BERT）。大多数DNA（约98%）是非编码DNA (ncDNA)，仅指导如何读取2%的编码DNA，这些DNA首先转录成mRNA，然后翻译成蛋白质。然而，许多复杂的医疗状况和精神健康状况都与ndna内的突变有关。非编码DNA （ncDNA）在统计学上与人类语言非常相似。DNA BERT已经利用了这一点。已有研究表明，可以用变分量子电路代替传统BERT模型的最终变压器层来承担分类任务。我们项目的目的是探索各种量子变压器混合模型，并将它们与经典模型进行比较。我们还将研究将基因组数据编码为量子态向量的更有效方法，并可能在此努力中考虑几何原理，不同的状态向量编码和压缩方法。目标是对ndna中与医疗状况相关的功能区域进行预测。基因组学是一个快速增长的细分市场，对未来的药物发现至关重要。虽然对于新疗法来说，ncDNA是一个更具挑战性的靶点，但研究表明，存在一些选择性分子可以结合这些区域并阻断这些区域。我们的目的是创建一个量子计算平台，支持在ndna序列中识别与疾病相关的功能区域。

Transformers and large language models have shown great potential in processing language-based information. Their strenth in connecting large and complex date sets makes them superior to conventional neural networks. Recently it has been shown that transformers (Google's BERT model) can also be applied to genomics datasets (DNA BERT). The majority of DNA (about 98%) is non-coding (ncDNA) and merely provides instructions on how to read the 2% of coding DNA that is first transcribed into mRNA and then translated into proteins. However, many complex medical conditions and also mental health conditions are associated with mutations within the ncDNA.The non-coding DNA (ncDNA) is statistically very similiar to human languages. This has been exploited with the DNA BERT. It has already been shown that the final transformer layer of the convential BERT model can be replaced with a variational quantum circuit to undertake classification tasks.The aim of our project is to explore various quantum transformer hybrids models and benchmark them against their classical counterparts. We will also investigate more efficient ways of encoding genomic data into the quantum state vector and may consider geometric principles, different state vector encoding and compression methods in this endeavour. The goal is to make predictions on functional regions within the ncDNA that have relevance for medical conditions.Genomics is a rapidly growing market segment and is essential in future drug discovery. Whilst ncDNA is a more challenging target for novel therapeutics, research has shown that selective molecules exist that can bind to these regions and block these regions. Our intention is to create a quantum computational platform that supports the identification of disease-relevant functional regions within ncDNA sequences.