arXiv subjects

Yi-Ming Huang

Publications and source records attributed to Yi-Ming Huang.

3 recordsLinked to original sources

arXiv2024

Molecular docking via quantum approximate optimization algorithm

Molecular docking plays a pivotal role in drug discovery and precision medicine, enabling us to understand protein functions and advance novel therapeutics. Here, we introduce a potential alternative solution to this problem, the digitized-counterdiabatic quantum approximate optimization algorithm (DC-QAOA), which utilizes counterdiabatic driving and QAOA on a quantum computer. Our method was applied to analyze diverse biological systems, including the SARS-CoV-2 Mpro complex with PM-2-020B, the DPP-4 complex with piperidine fused imidazopyridine 34, and the HIV-1 gp120 complex with JP-III-048. The DC-QAOA exhibits superior performance, providing more accurate and biologically relevant docking results, especially for larger molecular docking problems. Moreover, QAOA-based algorithms demonstrate enhanced hardware compatibility in the noisy intermediate-scale quantum era, indicating their potential for efficient implementation under practical docking scenarios. Our findings underscore quantum computing's potential in drug discovery and offer valuable insights for optimizing protein-ligand docking processes.

quant-ph↗

arXiv2018

Machine learning study of the relationship between the geometric and entropy discord

As an important resource to realize quantum information, quantum correlation displays different behaviors, freezing phenomenon and non-localization, which are dissimilar to the entanglement and classical correlation, respectively. In our setup, the ordering of quantum correlation is represented for different quantization methods by considering an open quantum system scenario. The machine learning method (neural network method) is then adopted to train for the construction of a bridge between the Rènyi discord ($α=2$) and the geometric discord (Bures distance) for $X$ form states. Our results clearly demonstrate that the machine learning method is useful for studying the differences and commonalities of different quantizing methods of quantum correlation.

quant-ph↗

arXiv2014

Network cluster detecting in associated bi-graph view

We find there is relationship between the associated bigraph and the cluster (or community) detecting on network. By imbedding the associated bigraph of some network (suppose it has cluster structures) into some space, we can identify the clusters on this network, which is a new method for network cluster detecting. And this method, of which the physical meaning is clear and the time complexity is acceptable, may provide us a new point to understand the structure and character of networks. In this paper, We test the methods on serval computer-generated networks and real networks. A computer-generated network with 128 vertexes and the Zachary Network, which presents the structure of a karate club, can be partitioned correctly by these methods. And the Dolphin Network, which presents the relationship between 62 dolphins on the coast of New Zealand, is partitioned reasonably.

physics.soc-ph↗