Optimize memory peak for _preprocess_state_vector in LightningTensor

[LuisAlfredoNu]

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---

Context:
After profiling Lightning Tensor with scalene profiler. We found a bottleneck of memory in the function _preprocess_state_vector (code) which allocates 3 arrays with dimension 2 ** wires * wires.

Description of the Change:
Optimize the cartesian product to reduce the amount of memory necessary to set the StatePrep with LTensor

Benefits:
Reduce by half the peak of memory for large systems close to 30 qubit

Benchmark code

    import pennylane as qml
    import numpy as np

    wires = 27

    state = np.random.rand(2**(wires-1))
    state = state / np.linalg.norm(state)

    dev = qml.device('lightning.tensor', wires=wires)

    dev_wires = dev.wires.tolist()

    @qml.qnode(dev)
    def circuit(state=state,dev_wires=dev_wires):
        qml.StatePrep(state, wires=dev_wires[1:])
        return qml.expval(qml.Z(0)), qml.state()

    return  circuit(state, dev_wires)

Possible Drawbacks:
This change reduces readability but with a good improvement.

Related GitHub Issues:
[sc-75692]

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[codecov]

Codecov Report

All modified and coverable lines are covered by tests ✅

Project coverage is 95.16%. Comparing base (4945ed0) to head (e3f65eb).
Report is 1 commits behind head on master.

Additional details and impacted files

@@            Coverage Diff             @@
##           master     #943      +/-   ##
==========================================
- Coverage   96.24%   95.16%   -1.09%     
==========================================
  Files         215       98     -117     
  Lines       29460    11922   -17538     
==========================================
- Hits        28353    11345   -17008     
+ Misses       1107      577     -530     

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[mlxd]

Feel free to ignore me if these comments are being a blocker. Otherwise, just looking for some clarifications :)

[AmintorDusko]

Thank you, @LuisAlfredoNu, for the very comprehensive explanation and in-office discussion. Happy to approve!

[mlxd]

💯