Scalable Quantum Neural Network builds and trains a large-scale QNN in a modular fashion. SQNN is evaluated with a binary classification task on the MNIST dataset.

This repository creates the codes from the PT_VQC and U-VQSVD algorithms for process tomography. Contains testing, continuous integration (CircleCi), full article see New J. Phys. 26 073017

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

A predictive algorithm to forecast the weather, chances of rain in particular, using a quantum approach.

A modular quantum computing library in Python featuring QAOA, Grover, HHL, and VQC — built on Qiskit, with future plans to become a QPU-agnostic, from-scratch quantum SDK.

Qiskit Fall Fest Kolkata Chapter Hackathon 2022 Project on QML

Quantum Machine Learning (QML) project that predicts suitable crops based on soil and environmental parameters using quantum-enhanced models. Built as a hybrid application combining classical preprocessing with quantum circuits (via Qiskit/PennyLane), this app demonstrates how quantum computing can be applied to real-world agricultural challenges.

Built a hybrid quantum–classical classifier for the Fashion-MNIST dataset using PyTorch and PennyLane, integrating a CNN feature extractor with a 4-qubit Variational Quantum Circuit.

Implementation of a Variational Quantum Classifier (VQC) to predict heart disease, created as the capstone project for the IBM and Freeya Mind Campus’ Road to Practitioner program.