20 citations · 36 across the 10 of their papers we have counts for
10 papers
Hybrid quantum-classical graph neural networks for tumor classification in digital pathology
Anupama Ray, Dhiraj Madan, Srushti Patil +2
Advances in classical machine learning and single-cell technologies have paved the way to understand interactions between disease cells and tumor microenvironments to accelerate th…
Towards quantum-enabled cell-centric therapeutics
Saugata Basu, Jannis Born, Aritra Bose +30
In recent years, there has been tremendous progress in the development of quantum computing hardware, algorithms and services leading to the expectation that in the near future qua…
Learning hard distributions with quantum-enhanced Variational Autoencoders
Anantha Rao, Dhiraj Madan, Anupama Ray +2
An important task in quantum generative machine learning is to model the probability distribution of measurements of many-body quantum systems. Classical generative models, such as…
Efficient Syndrome Decoder for Heavy Hexagonal QECC via Machine Learning
Debasmita Bhoumik, Ritajit Majumdar, Dhiraj Madan +3
Error syndromes for heavy hexagonal code and other topological codes such as surface code have typically been decoded by using Minimum Weight Perfect Matching (MWPM) based methods.…
Variational Learning for Unsupervised Knowledge Grounded Dialogs
Mayank Mishra, Dhiraj Madan, Gaurav Pandey +1
Recent methods for knowledge grounded dialogs generate responses by incorporating information from an external textual document. These methods do not require the exact document to…
Depth Optimized Ansatz Circuit in QAOA for Max-Cut
Ritajit Majumdar, Debasmita Bhoumik, Dhiraj Madan +3
While a Quantum Approximate Optimization Algorithm (QAOA) is intended to provide a quantum advantage in finding approximate solutions to combinatorial optimization problems, noise…
Optimizing Ansatz Design in QAOA for Max-cut
Ritajit Majumdar, Dhiraj Madan, Debasmita Bhoumik +3
Quantum Approximate Optimization Algorithm (QAOA) is studied primarily to find approximate solutions to combinatorial optimization problems. For a graph with vertices and e…
Unsupervised Learning of Interpretable Dialog Models
Dhiraj Madan, Dinesh Raghu, Gaurav Pandey +1
Recently several deep learning based models have been proposed for end-to-end learning of dialogs. While these models can be trained from data without the need for any additional a…
Finding Dominant User Utterances And System Responses in Conversations
Dhiraj Madan, Sachindra Joshi
There are several dialog frameworks which allow manual specification of intents and rule based dialog flow. The rule based framework provides good control to dialog designers at th…
Randomised Rounding with Applications
Dhiraj Madan, Sandeep Sen
We develop new techniques for rounding packing integer programs using iterative randomized rounding. It is based on a novel application of multidimensional Brownian motion in $\mat…