论文标题
化学模拟和药物发现的变分量子算法
Variational Quantum Algorithms for Chemical Simulation and Drug Discovery
论文作者
论文摘要
量子计算最近引起了很多关注，科学家已经使用量子计算在该领域中看到了潜在的应用，用于加密和与机器学习和医疗保健的通信。蛋白质折叠一直是最有趣的研究领域之一，也是生物化学最大的问题之一。每种蛋白质都会明显折叠，并且发现其稳定形状的困难随着链中氨基酸数量的增加而迅速增加。中等蛋白质具有约100个氨基酸，并且需要验证以发现稳定结构的组合数量很大。在某个时候，这些组合的数量将是如此之大，以至于古典计算机甚至无法尝试解决它们。在本文中，我们使用Qiskit性质，使用两种不同的量子量子本量（VQE）（VQE）（VQE）和量子近似优化算法（QAOA）研究了如何通过量子计算来解决此问题。我们比较了不同的量子硬件和模拟器的结果，并检查错误缓解如何影响性能。此外，我们与SOTA算法进行比较，并评估该方法的可靠性。
Quantum computing has gained a lot of attention recently, and scientists have seen potential applications in this field using quantum computing for Cryptography and Communication to Machine Learning and Healthcare. Protein folding has been one of the most interesting areas to study, and it is also one of the biggest problems of biochemistry. Each protein folds distinctively, and the difficulty of finding its stable shape rapidly increases with an increase in the number of amino acids in the chain. A moderate protein has about 100 amino acids, and the number of combinations one needs to verify to find the stable structure is enormous. At some point, the number of these combinations will be so vast that classical computers cannot even attempt to solve them. In this paper, we examine how this problem can be solved with the help of quantum computing using two different algorithms, Variational Quantum Eigensolver (VQE) and Quantum Approximate Optimization Algorithm (QAOA), using Qiskit Nature. We compare the results of different quantum hardware and simulators and check how error mitigation affects the performance. Further, we make comparisons with SoTA algorithms and evaluate the reliability of the method.
