Writing & Notes

A short history of quantum computing from Feynman to Shor, how "quantum machine learning" became its own field, and the four ways data and devices combine — CC, QC, CQ, QQ.

Before a quantum model can learn from classical data, the data has to get into the qubits. A tour of the encoding options — basis, amplitude, qsample, dynamic, angle — then a step-by-step build of basis encoding.

Where GANs sit among generative models, the generator-vs-discriminator game and its Nash equilibrium, how training actually works, the non-saturating cost trick, and the DCGAN recipe.