Reproducible QML benchmark: VQC vs QSVM on binary tasks. Modular, cross-platform pipeline w/ artifact logging.

Hybrid Classical–Quantum Neural Network for breast cancer detection from mammograms using DenseNet121 and Variational Quantum Circuits with noise robustness analysis.

Trainable, scalable quantum embedding kernels with variational parameters

implement the quantum support vector machine for classification using pennylane framework

Quantum-assisted pathology AI for cancer detection in whole slide images using PennyLane VQC + Graph Attention Networks on CAMELYON16

Quantum Filter Zoo: quantum filter layers (quanvolution, QPF, PQC conv) for PyTorch, built on PennyLane

Recurrent Neural Net built with PyTorch + trained on MNIST data

This project focuses on applying a Variational Quantum Circuit (VQC) for diabetes prediction using real-world medical data.

AI-powered early warning system for predicting climate-driven infectious disease outbreaks using Machine Learning and Quantum Machine Learning.

An amygdala hijack is a sudden, intense, and disproportionate emotional response to a situation that triggers the fight-or-flight response. It happens when the amygdala, a part of the brain that controls emotions and memories, takes over and disables the frontal lobes, which regulate rational thought,This project aims to give solutions related to.

This repository tackles the Banknote Authentication binary classification problem using both classical and quantum machine learning. Classical models (MLP) are implemented with scikit-learn and Keras, while a single-qubit variational circuit is built with Qiskit to compare performance.

This project simulates how a quantum bit (qubit) loses its information over time due to noise, using real physics equations (the Lindblad master equation) that describe how quantum systems decay and lose coherence. It then compares two ways of measuring a qubit's noise parameters (T1, T2, and detuning) from noisy experimental data: the traditional.