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Quantum computing architectures require an accurate and efficient descriptionin terms of many-electron states. Recent implementations include quantum dotarrays, where the ground state of a multi q-bit system can be altered byvoltages applied to the top gates. An extensive investigation concerning thespectra of the many-electron systems under multiple operation conditions set byexternal voltages typically requires a relatively large number of Hamiltoniandiagonalizations, where the Coulomb interaction is considered in an exactmanner. Instead of making exhaustive calculations using high throughputcomputing, we approach this problem by augmenting numerical diagonalizationswith machine learning techniques designed to predict the many-electroneigenvalues and eigenfunctions. To this end, we employ and compare the resultsfrom linear regression methods such as multivariate least squares (MLS) as wellas non-linear techniques based on kernel ridge regression (KRR), Gaussianprocess regression (GPR) and artificial neural networks (ANNs). The inputfeature vectors are assembled from readily available information comprised froma binary representation of the potential and the strength of the Coulombinteraction. Furthermore, employing a linear classifier, we establish a rulefor detecting a singlet-triplet transition which may arise for certainpotential configurations.

Investigation of bi-particle states in gate-array-controlled quantum-dot systems aided by machine learning techniques