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data-processing

dive_into_deep_learning

✔️李沐 【动手学深度学习】课程学习笔记：使用pycharm编程，基于pytorch框架实现。

ec-gan

EC-GAN: Low-Sample Classification using Semi-Supervised Algorithms and GANs (AAAI 2021)

qosf-mentorship

Screening Task 1

quantum-deep-learning

Recent advances in many fields have accelerated the demand for classification, regression, and detection problems from few 2D images/projections. Often, the heart of these modern techniques utilize neural networks, which can be implemented with deep learning algorithms. In our neural network architecture, we embed a dynamically programmable quantum circuit, acting as a hidden layer, to learn the correct parameters to correctly classify handwritten digits from the MNIST database. By starting small and making incremental improvements, we successfully reach a stunning ~95% accuracy on identifying previously unseen digits from 0 to 7 using this architecture!

quantum-generative-adversarial-network

A Quantum State Fidelity based Generative Adversarial Network

quantum-neural-network

Qiskit Hackathon Korea 2021 Community Choice Award Winner : Exploring Hybrid quantum-classical Neural Networks with PyTorch and Qiskit

quantumflow_tutorial

A step-by-step tutorial of QuantumFlow, using the MNIST sub-dataset {3,6} and the 16-2-2 neural network as an example.

simple-qcnn

zwyzhr

Config files for my GitHub profile.