Bayesian autotuning of Hubbard model quantum simulators

Abstract

Spins in gated semiconductor quantum dots (QDs) are a promising platform for Hubbard model simulation inaccessible to computation. Precise control of the tunnel couplings by tuning voltages on metallic gates is vital for a successful QD-based simulator. However, the number of tunable voltages and the complexity of the relationships between gate voltages and the parameters of the resulting Hubbard models quickly increase with the number of quantum dots. As a consequence, it is not known if and how a particular gate geometry yields a target Hubbard model. To solve this problem, we propose a hybrid machine-learning approach using a combination of support vector machines (SVMs) and Bayesian optimization (BO) to identify combinations of voltages that realize a desired Hubbard model. SVM constrains the space of voltages by rejecting voltage combinations producing potentials unsuitable for tight-binding (TB) approximation. The target voltage combinations are then identified by BO in the constrained subdomain. For large QD arrays, we propose a scalable efficient iterative procedure using our SVM-BO approach, which optimises voltage subsets and utilises a two-QD SVM model for large systems. Our results use experimental gate lithography images and accurate integrals calculated with linear combinations of harmonic orbitals to train the machine learning algorithms.