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Md. Zubair

Publications and source records attributed to Md. Zubair.

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Point-group filtering in unitary coupled-cluster ansätze: exact criticality under the Abelian filter, freeness measured near equilibrium under the full group

Filtering a unitary coupled-cluster ansatz by molecular point-group symmetry has no settled variational cost for groups with degenerate irreducible representations. We prove that the symmetry-adapted subvariety of the amplitude space is a critical subvariety of the energy, for irreducible representations of any dimension. The gradient along every removed direction vanishes there, so a quasi-Newton optimisation with exact gradients started at the reference determinant cannot distinguish the filtered from the unfiltered ansatz. Every operator invariant under the full point group already lies in the span of the pool retained by an Abelian subgroup, so the filtered pool reaches at every geometry an energy no higher than the fully invariant one reaches. A Hamiltonian-informed pool lies inside the set the equivariant pool touches, and in a symmetry-adapted basis inside the symmetry-filtered pool, which makes a published pair of parameter counts a certificate for the orbital basis. Across six molecules and two basis sets, the full-group filter changes the classical coupled-cluster correlation energy by at most $3.4 \times 10^{-12}$ millihartree in the larger basis where the point group is finite. The parameter count of the unitary ansatz falls from 75 to 30 for ammonia and from 65 to 21 for methane, measured against the Abelian filter current practice uses. The freeness measured for the full-group filter holds near equilibrium and degrades into the static-correlation regime.

quant-ph

An Efficient K-means Clustering Algorithm for Analysing COVID-19

COVID-19 hits the world like a storm by arising pandemic situations for most of the countries around the world. The whole world is trying to overcome this pandemic situation. A better health care quality may help a country to tackle the pandemic. Making clusters of countries with similar types of health care quality provides an insight into the quality of health care in different countries. In the area of machine learning and data science, the K-means clustering algorithm is typically used to create clusters based on similarity. In this paper, we propose an efficient K-means clustering method that determines the initial centroids of the clusters efficiently. Based on this proposed method, we have determined health care quality clusters of countries utilizing the COVID-19 datasets. Experimental results show that our proposed method reduces the number of iterations and execution time to analyze COVID-19 while comparing with the traditional k-means clustering algorithm.

cs.LG