74 citations · 90 across the 7 of their papers we have counts for
7 papers
Alz-QNet: A Quantum Regression Network for Studying Alzheimer's Gene Interactions
Debanjan Konar, Neerav Sreekumar, Richard Jiang +1
Understanding the molecular-level mechanisms underpinning Alzheimer's disease (AD) by studying crucial genes associated with the disease remains a challenge. Alzheimer's, being a m…
A Scalable Quantum Non-local Neural Network for Image Classification
Sparsh Gupta, Debanjan Konar, Vaneet Aggarwal
Non-local operations play a crucial role in computer vision enabling the capture of long-range dependencies through weighted sums of features across the input, surpassing the const…
A Bi-directional Multi-solution Scalable Grover Search Algorithm
Debanjan Konar, Zain Hafeez, Vaneet Aggarwal
Grover's search algorithms, including various Partial Grover Searches (PGS), suffer from scaling issues when multiple solutions are sought, as the number of iterations scales with…
Tensor Ring Optimized Quantum-Enhanced Tensor Neural Networks
Debanjan Konar, Dheeraj Peddireddy, Vaneet Aggarwal +1
Quantum machine learning researchers often rely on incorporating Tensor Networks (TN) into Deep Neural Networks (DNN) and variational optimization. However, the standard optimizati…
A Scalable 5,6-Qubit Grover's Quantum Search Algorithm
Dinesh Reddy Vemula, Debanjan Konar, Sudeep Satheesan +2
Recent studies have been spurred on by the promise of advanced quantum computing technology, which has led to the development of quantum computer simulations on classical hardware.…
Random Quantum Neural Networks (RQNN) for Noisy Image Recognition
Debanjan Konar, Erol Gelenbe, Soham Bhandary +2
Classical Random Neural Networks (RNNs) have demonstrated effective applications in decision making, signal processing, and image recognition tasks. However, their implementation h…
Qutrit-inspired Fully Self-supervised Shallow Quantum Learning Network for Brain Tumor Segmentation
Debanjan Konar, Siddhartha Bhattacharyya, Bijaya K. Panigrahi +1
Classical self-supervised networks suffer from convergence problems and reduced segmentation accuracy due to forceful termination. Qubits or bi-level quantum bits often describe qu…