viventriglia/Quantum_Neural_Network_QNN
Does adding quantum features improve the overall performance of a neural network?
This project explores whether integrating quantum features can enhance the performance of neural networks, particularly for image processing tasks. It takes an input image and processes small regions through a quantum circuit, producing a new multi-channel image. This system is designed for machine learning researchers and quantum computing enthusiasts who are experimenting with hybrid quantum-classical models for image classification.
No commits in the last 6 months.
Use this if you are a researcher in quantum machine learning or AI, looking to investigate the benefits of quantum convolutional layers for image classification over traditional methods.
Not ideal if you need a production-ready, highly optimized image classification system without a focus on quantum computing experimentation.
Stars
20
Forks
10
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Jul 08, 2021
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/viventriglia/Quantum_Neural_Network_QNN"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
PennyLaneAI/pennylane
PennyLane is an open-source quantum software platform for quantum computing, quantum machine...
qiskit-community/qiskit-machine-learning
An open-source library built on Qiskit for quantum machine learning tasks at scale on quantum...
netket/netket
Machine learning algorithms for many-body quantum systems
tencent-quantum-lab/tensorcircuit
Tensor network based quantum software framework for the NISQ era
mit-han-lab/torchquantum
A PyTorch-based framework for Quantum Classical Simulation, Quantum Machine Learning, Quantum...