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An active area of investigation in the search for quantum advantage isQuantum Machine Learning. Quantum Machine Learning, and Parameterized QuantumCircuits in a hybrid quantum-classical setup in particular, could bringadvancements in accuracy by utilizing the high dimensionality of the Hilbertspace as feature space. But is the ability of a quantum circuit to uniformlyaddress the Hilbert space a good indicator of classification accuracy? In ourwork, we use methods and quantifications from prior art to perform a numericalstudy in order to evaluate the level of correlation. We find a strongcorrelation between the ability of the circuit to uniformly address the Hilbertspace and the achieved classification accuracy for circuits that entail asingle embedding layer followed by 1 or 2 circuit designs. This is based on ourstudy encompassing 19 circuits in both 1 and 2 layer configuration, evaluatedon 9 datasets of increasing difficulty. Future work will evaluate if this holdsfor different circuit designs.

Evaluation of Parameterized Quantum Circuits: on the relation between classification accuracy, expressibility and entangling capability