349 citations · 851 across the 11 of their papers we have counts for
11 papers
An Algorithm for Fast Supervised Learning in Variational Circuits through Simultaneous Processing of Multiple Samples
Siddharth Dangwal, Ritvik Sharma, Debanjan Bhowmik
We propose a novel algorithm for fast training of variational classifiers by processing multiple samples parallelly. The algorithm can be adapted for any ansatz used in the variati…
Supervised Learning Using a Dressed Quantum Network with "Super Compressed Encoding": Algorithm and Quantum-Hardware-Based Implementation
Saurabh Kumar, Siddharth Dangwal, Debanjan Bhowmik
Implementation of variational Quantum Machine Learning (QML) algorithms on Noisy Intermediate-Scale Quantum (NISQ) devices is known to have issues related to the high number of qub…
Comparing domain wall synapse with other Non Volatile Memory devices for on-chip learning in Analog Hardware Neural Network
Divya Kaushik, Utkarsh Singh, Upasana Sahu +2
Resistive Random Access Memory (RRAM) and Phase Change Memory (PCM) devices have been popularly used as synapses in crossbar array based analog Neural Network (NN) circuit to achie…
Supervised learning with a quantum classifier using a multi-level system
Soumik Adhikary, Siddharth Dangwal, Debanjan Bhowmik
We propose a quantum classifier, which can classify data under the supervised learning scheme using a quantum feature space. The input feature vectors are encoded in a single qu…
On-chip learning in a conventional silicon MOSFET based Analog Hardware Neural Network
Nilabjo Dey, Janak Sharda, Utkarsh Saxena +3
On-chip learning in a crossbar array based analog hardware Neural Network (NN) has been shown to have major advantages in terms of speed and energy compared to training NN on a tra…
On-chip learning for domain wall synapse based Fully Connected Neural Network
Apoorv Dankar, Anand Verma, Utkarsh Saxena +3
Spintronic devices are considered as promising candidates in implementing neuromorphic systems or hardware neural networks, which are expected to perform better than other existing…