Research On Quantum Computation And Its Application To Biology And Chemistry Problems

As the informatization process advances swiftly and vigorously,people demand higher speeds and better performance in information processing.For instance,in the research domain of biology and chemistry,there are many problems with big data need us solve urgently,such as similarity measures for molecules,PPI networks alignment,et al.Although the performance of modern computers has improved to hundreds of millions of operations per second,it can't meet people's needs.However,the theory of quantum computation based on quantum mechanics becomes a new hope to meet this need.The thory also lays theoretical foundation for producing the quantum computers.Because the quantum computation represented by quantum algorithms has specific and inherent parallelism,entanglement,exponential storage capacity and exponential speedup compared with classical algorithms,it has great superiority and strong vitality.This paper focuses on several hot topics which need to process mass data and require high parallel algorithms in the biology and chemistry domain.The quantum algorithms to solve the problems mentioned above are investigated.The content of this paper can be summed up in detail as following.Firstly,we study quantum Fourier transform and quantum phase estimation.After the problem about similarity measures for molecules is described formally,we investigate how to obtain similarity measures for molecules using quantum phase estimation.The corresponding quantum algorithm QMSM is introduced.We analyze the time complexity and success probability and show that QMSM has better performance than relevant classical algorithm.Secondly,we study the quantum random walk model.We also study the algorithm QUID which can ascertain whether two graphs are isomorphic or not and another algorithm PIMS which can find a specific isomorphism mapping between isomorphic graphs.We combine these two algorithms QUID and PIMS with biological features of PPI networks,and then we propose the quantum algorithm QPSS to search structural similar subnets in PPI networks.An executing instance of QPSS algorithm is demonstrated.Moreover,we discuss the performance evaluation of this algorithm.After the time complexity of QPSS algorithm compares with its classical counterpart,it has been proved that the QMSM obtains a nearly quadratic speedup.Then,after we study the quantum derived neural network model HQNN and the quantum ant colony optimization algorithm CQACA,we first propose the quantum neural computing scheme named CQACA-HQNN which integrates HQNN and CQACA.In this scheme the algorithm CQACA is utilized to optimize the parameters of HQNN.This scheme improves convergence speed of parameter optimization and avoids local optimal solutions.Finally,we investigate how to apply the scheme CQACA-HQNN to predict protein-protein interaction(PPI).We determine the training set and the test set based on biological characteristics of the PPI prediction problem.The quantum derived neural network HQNN is constructed and the parameters of this network are set.Then we utilize the quantum ant colony optimization algorithm CQACA to train the rotation angles of middle layer and the weights of output layer in HQNN.Simulation results show that this scheme CQACA-HQNN obtains better prediction effect than those methods in other literature.