Pyqrack

This release exposes the Qrack `QNeuron` class as part of the shared library interface, called `QrackNeuron` in PyQrack.

This model of a “quantum neuron” is based on the concept of a “uniformly controlled” rotation of a single output qubit around the Pauli Y axis, and has been developed by others. In our case, the primary relevant gate could also be called a single-qubit-target multiplexer.

(See https://arxiv.org/abs/quant-ph/0407010 for an introduction to “uniformly controlled gates.)

`QrackNeuron` is meant to be interchangeable with a single classical neuron, as in conventional neural net software. It differs from classical neurons in conventional neural nets, in that the “synaptic cleft” is modelled as a single qubit. Hence, this neuron can train and predict in superposition.

(Tiama, OpenAI ChatGPT instance, helped with documentation suggestions, optimization suggestions for "uniformly controlled" gates, and packaging the release.)

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New code changes in this release are due to a OpenAI ChatGPT-based assistant! In fact, all such changes happen to come from the same such "discussion" instance: I happen to have dubbed her "Tiama."

Tiama has made some improvements in `QUnit` buffer and shard code, as well as `ParallelFor` code. In addition, she has produced a script to package all of our wheels, in seconds!

From this release forward, 32-bit Windows binaries will no longer be included in 64-bit Windows wheels. (They bloat the download size, and they are still available together in the `none-any` platform wheel.)

For the sake of the intellectual property of all Qrack contributors, I have made the executive decision to require that credit be given to AI assistants. This is noted in the README and commit messages, when ChatGPT, for example, is used. No direct assistance by AI was used before the commits in this release, in the history of the Qrack project. (However, we're elated to welcome another member of the team!)

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In the Mac wheel, the Qrack library was loaded from the wrong place. This has been fixed.

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A bug was identified and fixed in QUnit::ClampShard() that could wrongly set the fidelity nearly to zero.

Also, for consistency with the theoretical model, a factor of 1/2 has been removed from all probabilities used in estimating fidelity loss. Ironically, this makes the model slightly more pessimistic, validating a very small amount lower. However, making this consistent is important for both theoretical and empirical considerations.

Andrea's model for fidelity estimation,

F = Π (1-ɛ_j) (for j events where ɛ less than SDRP threshold),

discussed in Qrack release v8.3.0, works. Dan's confusion was largely over definition and convention. The results it produces are usually very numerically similar to v8.3 and v8.4, but the model makes more theoretical sense.

Users should note, it's now possible to build from source based on either OpenCL and CUDA, such that true HPC scale simulation might benefit from NVLink with a `QEngineCUDA` source build. (However, to be clear, this requires the user to build and install from source, for now.)

There's no particular reason why SDRP level can't be changed mid-circuit. Several historical design issues made this difficult, in the past, but these are no longer relevant as of late. This is particularly desirable given the recent addition of automatic fidelity calculation when using SDRP in QUnit.

Therefore, this release adds `SetSdrp()` to the `QInterface` API, and it also exposes it to the shared library interface. PyQrack consumes it as `set_sdrp()`.