Added Entanglement Concentration Dataset for 3 and 4 qubits for Benchmarking Binary Classifiers

[RishiNandha]

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Summary

Classification dataset for 3 and 4 qubits based on the concentration of entanglement (CE) in Quantum States. Two pre-trained circuits are used to generate states of a given amount of CE. Users can use this dataset to benchmark their binary classification pipelines.

Pre-trained weights courtesy to https://github.com/LSchatzki/NTangled_Datasets. The CE values claimed in the above repository had a mismatch for 8 qubits, hence we've left other number of qubits for future development

I've verified mypy, spell, lint and black. For some reason, make html breaks the other modules. Need some help with resolving that

Details and comments

• We have made the order and default values of parameters match the existing ad hoc data generator for consistency. Hence equal number of datapoints in each class are generated.
• There are two sampling options: the input states given to the circuit before it's action can either be sampled by setting each qubit's state as one of the axes of the bloch sphere ("cardinal") or can be sampled randomly ("isotropic")
• Each qubit has an easy and a hard mode. Easy has a larger difference in CE values than hard. This is to make benchmarking of pipelines more standardizable. Easy can be used to verify the working of algorithms, while hard can be used to test the maximum the algorithm can achieve.
• There are two formatting options. The x_train and x_test can either be a numpy array or a list of quantum states
• Reference for confirming the relevance of such a dataset: https://arxiv.org/abs/2109.03400. Authors have shown that QCNNs can learn from these datasets effectively