Tutorials

Eight pages, in order. Each one builds on the last, and each is short enough to read in a sitting.

Every Python snippet on these pages is executed by the test suite. They are not illustrations of the API — they are tests of it, so a rename that breaks a tutorial breaks the build. The outputs shown were produced by running the code, not written by hand.

• 1. Circuits are data

  Build, draw, run, measure. The CircuitSpec/slot idea that everything else depends on, and why shots come with an error bar.

• 2. Getting data in

  Angle, basis, amplitude and Pauli feature maps — the choice that fixes what your model can represent, before training starts.

• 3. Gradients

  Six methods, one function. Plus the two ways a hand-rolled parameter-shift returns a smooth, plausible, wrong answer.

• 4. Designing an ansatz

  A block vocabulary where a new ansatz is one line and inherits gradients, resource counting and a torch layer for free.

• 5. Training with PyTorch

  VQC in two lines, QuantumLayer in any nn.Module, and the input gradient that decides whether your classical pre-net trains at all.

• 6. Quantum kernels

  No variational training, a convex solver — and a quadratic circuit cost plus exponential concentration waiting for you.

• 7. Re-uploading and Fourier

  Why depth buys frequencies, measured rather than asserted, and the commuting block that silently collapses the whole model.

• 8. Trainability

  Barren plateaus, cost locality, and three optimisers that exploit structure a general-purpose one cannot see.

Running them yourself

Everything except tutorials 5 and 6 needs only a bare install:

pip install qmlkit

Tutorial 5 needs qmlkit[torch], and the classification section of tutorial 6 needs qmlkit[sklearn]. Both pages say so where it matters.

If you would rather have one script than eight pages, the repository ships examples/quickstart.py, which walks the same ground end to end.