What are quantum computers, and how can we train them in Python?
summary
Python code shows how to build parameterized quantum functions using PennyLane (and mentions Qiskit, Cirq), run them on a local simulator or cloud hardware, extract gradients with the parameter-shift rule, and plug them into optimizers and ML frameworks. A PyTorch autograd wrapper is implemented so hybrids backpropagate; an example optimization finishes in 2 iterations with ~32 quantum evaluations and lowers the cost from 0.81 to −0.84, so the approach actually works. PennyLane + PyTorch
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chapters (9)
- 0:00Start
- 1:28What Are Quantum Computers
- 1:39What Quantum Computers Are
- 6:36Build a Quantum Function in Python
- 8:59Create Our Quantum Function
- 15:08Parameter Shift Rule
- 19:24Backwards Method
- 22:51Back Propagation
- 24:59Resources
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