quantum-neural-network
Exploring Hybrid quantum-classical Neural Networks with PyTorch and Qiskit
🎉Qiskit Hackathon Korea 2021 : Community Choice Award Winner🎉
Team "Quanputing"
| name | github | role |
|---|---|---|
| Kifumi Numata | @kifumi | Coach, Qiskit Advocate |
| Anna Phan | @attp | Coach, Qiskit Advocate |
| Dohun Kim | @yh08037 | Code development - model1/model2 |
| Yunseo Kim | @Yunseo47 | Code development - model2, Presentation |
| Jaehoon Hahm | @Jaehoon-zx | Create presentation slides, Presentation |
| DaeHeon Yoon | @Greathoney | Code development - model1, Create presentation slides |
| Yoon Kwon | @vhapfks | Create presentation slides |
| Eunchan Lee | @purang2 | Code development - model1 |
Model 1. CNN with Quantum Fully Connected Layer
Build MNIST multi-label classifiers using classical convolution layers and quantum fully-connected layers.
Model 2. CNN with Quantum Convolution Layer
Build MNIST multi-label classifiers using quantum convolution layers and classical fully-connected layers.
References
Model 1. CNN with Quantum Fully Connected Layer
- Hybrid quantum-classical Neural Networks with PyTorch and Qiskit (Qiskit textbook)
- Gradients of parameterized quantum gates using the parameter-shift rule and gatedecomposition (arxiv)