I Used the Variational Quantum Eigensolver (VQE) to find the ground state of a 4by4 matrix hamiltonian which we will use two qubits for it. The VQE algorithm is run in a noisy and noiseless simulator. The code implementation is written with the Qiskit language.

Repository for `Glassy dynamics using Quantum Computers` Team in Qiskit Hackathon Europe

Implementations of quantum variational classifier and quantum kernel estimator proposed in Havlíček, V., Córcoles, A.D., Temme, K. et al. Supervised learning with quantum-enhanced feature spaces. Nature 567, 209–212 (2019). https://doi.org/10.1038/s41586-019-0980-2

Quantum Machine Learning is Quantum Computing + Machine Learning 🧠. There are various benefits associated with using QML: It helps to speed up the training process, working on high dimensional data is possible and more 📊.

Pulmonary Fibrosis Prognosis Prediction utilising Quantum Machine Learning - Variational Circuits

Codebase of the Fibro-QuanNet: Pulmonary Fibrosis Prognosis Prediction using Quantum Machine Learning

Training iterated protocols for distillation of GHZ states with variational quantum algorithms.

Screening task submission for the QOSF mentorship

An implementation of Quantum Variational Circuits both in Full-Quantum and Hybrid contexts using PyTorch and Pennylane.