1 code implementation • 24 Apr 2024 • Debanjan Konar, Zain Hafeez, Vaneet Aggarwal
Grover's search algorithms, including various partial Grover searches, experience scaling problems as the number of iterations rises with increased qubits, making implementation more computationally expensive.
1 code implementation • 2 Oct 2023 • Debanjan Konar, Dheeraj Peddireddy, Vaneet Aggarwal, Bijaya K. Panigrahi
Quantum machine learning researchers often rely on incorporating Tensor Networks (TN) into Deep Neural Networks (DNN) and variational optimization.
no code implementations • 30 Apr 2022 • Dinesh Reddy Vemula, Debanjan Konar, Sudeep Satheesan, Sri Mounica Kalidasu, Attila Cangi
Grover's quantum search algorithm is one of the well-known applications of quantum computing, enabling quantum computers to perform a database search (unsorted array) and quadratically outperform their classical counterparts in terms of time.
1 code implementation • 3 Mar 2022 • Debanjan Konar, Erol Gelenbe, Soham Bhandary, Aditya Das Sarma, Attila Cangi
We have extensively validated our proposed RQNN model, relying on hybrid classical-quantum algorithms via the PennyLane Quantum simulator with a limited number of \emph{qubits}.
no code implementations • 14 Sep 2020 • Debanjan Konar, Siddhartha Bhattacharyya, Bijaya K. Panigrahi, Elizabeth Behrman
Classical self-supervised networks suffer from convergence problems and reduced segmentation accuracy due to forceful termination.