variational-circuit

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 📊.

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

Applied quantum kernels for anomaly detection. Low-data anomaly detection on manifold-structured telemetry, benchmarking entanglement kernels vs classical baselines with geometric diagnostics.

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

Pulmonary Fibrosis Prognosis Prediction utilising Quantum Machine Learning - Variational Circuits

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

This project focuses on developing a Variational Quantum Circuit capable of performing Binary Classification between two classes: red wine and white wine, based on their characteristics using machine learning.

Screening task submission for the QOSF mentorship