Quantum Neural Network (QNN), Variational Quantum Eigensolver (VQE)

QNN 1 Neural Network Regressor qiskit

Implementation of a Quantized Neural Network with low bitwidth of weights, activations and gradients.

A modular and extensible quantum machine learning framework supporting customizable models, encoders, kernels, and quantum providers for research and prototyping.

Patched Qualcomm AI Stack SDK fixing critical memory corruption and shape inference bugs in model converter tools (QNN/SNPE).

Image processing with advanced GANs and QNNs

Comparative analysis of CNN and Hybrid Quantum Neural Network (QNN) architectures for electrical resistivity prediction using synthetically generated ERT data. Includes data generation, preprocessing, classical deep learning, quantum-enhanced modeling, and performance evaluation.

Notebooks for the Seminar in ETSIT (UPM) - Master of Science in Signal Theory and Communication (Track: SIGNAL PROCESSING AND MACHINE LEARNING FOR BIG DATA).

💎 REAL-TIME MUSICAL STEM SEPARATION 💎 Legacy S24 Ultra Edition. Optimized for Snapdragon 8 Gen 3 (Cortex-X4). Discontinued due to device theft.

"Not So Hermitian" is a Team came together for quantum computing stuff. Members: Yusuf Aykut, Ömer Faruk Rüzgar, Ahmet Tezcek, Yiğit Angı ve Ibrahim Abushawish

Diabetic Retinotherapy using Quantum Computing

Automatic build onnxruntime+qnn with Java support. The package contains aar,headers and shared libraries.

This work explores whether the quanvolution neural network is explainable by proposing a novel mathematical approach for quantifying explainability

Quantum Neural Network Regressor (QNN Regressor) - Variational Quantum Regressor (VQR)

Testing and playing around with QNNs using the mnist dataset

Qualcomm lib Genie binding for Node.js

collection of hybrid quantum-classical machine learning applications

Supplementary information about the paper "Exploring Quantum Machine Learning for Weather Forecasting". Authors: Maria Heloísa F. da Silva, Gleydson F. de Jesus, Christiano M. S. Nascimento, Valéria L. da Silva, Clebson S. Cruz. Code based on Ogur (2023)¹ for QNNs and Ekman (2021)² for RNNs, with Datasets from NASA POWER platform.

An HW-SW codesign tool to develeop and deploy MIxed-Precision QNN on PULP-based SoC's.

Contains the source code for the paper: Exploring Channel Distinguishability in Local Neighborhoods of the Model Space in Quantum Neural Networks