Data Complexity Measures for Quantum Circuits Architecture Recommendation
This work addresses the challenge of efficiently designing quantum circuits for classification problems, though it appears incremental as it applies existing data complexity measures to a new quantum context.
The authors tackled the problem of selecting optimal quantum circuit architectures for classification by using data complexity measures to recommend circuits and layer repetitions, achieving up to 100% accuracy and a mean absolute error of 0.80 ± 2.17 for layer prediction.
Quantum Parametric Circuits are constructed as an alternative to reduce the size of quantum circuits, meaning to decrease the number of quantum gates and, consequently, the depth of these circuits. However, determining the optimal circuit for a given problem remains an open question. Testing various combinations is challenging due to the infinite possibilities. In this work, a quantum circuit recommendation architecture for classification problems is proposed using database complexity measures. A quantum circuit is defined based on a circuit layer and the number of times this layer is iterated. Fourteen databases of varying dimensions and different numbers of classes were used to evaluate six quantum circuits, each with 1, 2, 3, 4, 8, and 16-layer repetitions. Using data complexity measures from the databases, it was possible to identify the optimal circuit capable of solving all problems with up to 100$\%$ accuracy. Furthermore, with a mean absolute error of 0.80 $\pm$ 2.17, one determined the appropriate number of layer repetitions, allowing for an error margin of up to three additional layers. Sixteen distinct machine learning models were employed for the selection of quantum circuits, alongside twelve classical regressor models to dynamically define the number of layers.