Github Charanpanthangi Quantum Clustering This project compares classical k means clustering with a simple quantum enhanced version. we map data to quantum states and use a fidelity based quantum distance instead of euclidean distance. I am a data scientist and lead engineer based in bangalore, focused on building end to end ai and analytics solutions. my work spans machine learning, nlp, gena charanpanthangi.
Github Chayanpatra Clustering It’s simply my story of exploring qml through 20 hands on projects — the ones that finally made the field feel intuitive, exciting, and real. Data scientist | lead engineer | ai & genai builder | quantum computing enthusiast. i work at the intersection of ai, machine learning, genai, and engineering, building scalable, production ready solutions that solve real world problems. Contribute to charanpanthangi quantum distance based classifier development by creating an account on github. Quantum algorithms are expected to surpass their classical counterparts in terms of computational complexity for certain tasks, including machine learning. in this paper, we design, implement, and evaluate three hybrid quantum k means algorithms, exploiting different degree of parallelism.
Github Jean Baptistedubois Quantum Inspired Clustering This Contribute to charanpanthangi quantum distance based classifier development by creating an account on github. Quantum algorithms are expected to surpass their classical counterparts in terms of computational complexity for certain tasks, including machine learning. in this paper, we design, implement, and evaluate three hybrid quantum k means algorithms, exploiting different degree of parallelism. This demo encodes simple classical data into quantum states, builds a density matrix, and extracts dominant eigenvalues. the quantum like outputs are compared with classical pca. This repository shows how to reuse a parameterized quantum circuit (pqc) trained on one "source" classification task for a related "target" task. a small classical classifier head is placed on top of the pqc outputs. Contribute to charanpanthangi quantum graph neural network development by creating an account on github. Beginner friendly example showing how a small parameterized quantum circuit can learn a smooth function such as y = sin (2πx). the project also includes a simple classical regressor for comparison, svg only plots, and a notebook tutorial.
Quantum Programming Github This demo encodes simple classical data into quantum states, builds a density matrix, and extracts dominant eigenvalues. the quantum like outputs are compared with classical pca. This repository shows how to reuse a parameterized quantum circuit (pqc) trained on one "source" classification task for a related "target" task. a small classical classifier head is placed on top of the pqc outputs. Contribute to charanpanthangi quantum graph neural network development by creating an account on github. Beginner friendly example showing how a small parameterized quantum circuit can learn a smooth function such as y = sin (2πx). the project also includes a simple classical regressor for comparison, svg only plots, and a notebook tutorial.
Github Andrekev Quantum Data Science And Cryptography At A Quantum Contribute to charanpanthangi quantum graph neural network development by creating an account on github. Beginner friendly example showing how a small parameterized quantum circuit can learn a smooth function such as y = sin (2πx). the project also includes a simple classical regressor for comparison, svg only plots, and a notebook tutorial.
Github Varahakrishna Clustering Performed Hierarchical Clustering
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