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2022_qutech_challenge

MIT iQuHACK 2022 x QuTech Challenge

binary-image-classifier

The project works upon classifying images into two distinct classes of MNIST dataset using binary encoding and Quantum Neural Network.

bloqq

covidprotease

iQuHack 2022 Project

flask-app-with-fakenewsdetection-using-neuralnetworks

meme-stream

os-virtual-lab

Virtual Lab GUI to simulate, learn and experiment with Operating System Algorithms.

qchack-2022

qhack-2022

The variational quantum eigensolver (VQE) is generally used for finding the ground state energy of a given hamiltonian. To find the kth excited state energy of the hamiltonian we need to run the VQE optimization process at least k+1 times. Each time we also need to calculate the hamiltonian again, taking into account the state of the previous iteration. Even after that, the accuracy decreases as the value of k increases. The Subspace Search VQE (SSVQE) algorithm is used to find the kth excited-state energy of a hamiltonian in two subsequent optimization processes. Research on a more generalized version of SSVQE, namely Weighted SSVQE, shows that by using the weights as hyperparameters we can find the kth excited-state energy in just a single optimization process. There are two variants of this algorithm: Weighted SSVQE to find kth excited state energy, and weighted SSVQE to find all energies up to the kth excited state.

qhack-2023

Ground State Energy of BeH2 at different bond lengths using VQE

quantum-visualizer

This application is meant for the visualization of the various Quantum gates on a qubit. It provides a Simulation of the Qubit transform on the Bloch Sphere via animation. The goal of this project is to add new functionalities to this application.

voice_assisstant