quantum-machine-learning

Feature details

Due to the similarity, it is easy to confuse qml.X and qml.PauliX, especially since other methods of specifying circuits, e.g., QASM, use x for PauliX. But if a user uses qml.X in their circuit on a qubit device, nothing happens to inform them that the incorrect operation is being used:

@qml.qnode(dev)
def circ():
    qml.PauliX(wires=0)
    qml.Hada

The init module has been deprecated, and the recommend approach for generating initial weights is to use the Template.shape method:

>>> from pennylane.templates import StronglyEntanglingLayers
>>> qml.init.strong_ent_layers_normal(n_layers=3, n_wires=2) # deprecated
>>> np.random.random(StronglyEntanglingLayers.shape(n_layers=3, n_wires=2))  # new approach

We should upd

начало обсуждения тут

Follow up for #197 and #215

• Fix the ORDER BY issue so that we don't accidentally overwrite extra dispatch IDs https://github.com/AgnostiqHQ/covalent/pull/215/files#diff-d5a1b52ef809a88edbb01367cf4a161ddc0b9ce35c885981d8a8363cb0588949R151
• Figure out why middleware doesn't work https://github.com/AgnostiqHQ/covalent/pull/215/files#diff-d5a1b52ef809a88edbb01367cf4a161ddc0b9ce35c8859

Improve QFT part with other non-Fourier transform (wavelet, fourier on groups..

It would be cool to have some non-trivial facts about the QFT

(like how to see it as a mapping between elements of a group to a Hilbert space).

But also it would be even better to have more quantum transform, like the Wavelet transfrom.

This can be really helpful in the context of group-theoretical machine learnin

LGTM has flagged a few code quality issues. Fix em.

https://lgtm.com/projects/g/gecrooks/quantumflow-dev/context:python

Describe the bug
The example at https://ichec.github.io/QNLP/examples/gate_ops/ has a mistake in notation for S4 and S_i at the end of page.

Expected behavior
Si=σxRY(cos−1((i−2)/2)))σx and Si=RY(−cos−1((i−2)/2))) should be Si=σxRY(cos−1((i−2)/i)))σx and Si=RY(−cos−1((i−2)/i))) respectively.

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