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Quantum information processing is likely to have far-reaching impact in thefield of artificial intelligence. While the race to build an error-correctedquantum computer is ongoing, noisy, intermediate-scale quantum (NISQ) devicesprovide an immediate platform for exploring a possible quantum advantagethrough hybrid quantum--classical machine learning algorithms. One example ofsuch a hybrid algorithm is "quantum kitchen sinks", which builds upon theclassical algorithm known as "random kitchen sinks" to leverage a gate modelquantum computer for machine learning applications. We propose an alternativealgorithm called "adiabatic quantum kitchen sinks", which employs an adiabaticquantum device to transform data features into new features in a non-linearmanner, which can then be employed by classical machine learning algorithms. Wepresent the effectiveness of our algorithm for performing binary classificationon both a synthetic dataset and a real-world dataset. In terms ofclassification accuracy, our algorithm significantly enhances the performanceof a classical linear classifier on the studied binary classification tasks andcan potentially be implemented on a current adiabatic quantum device to solvepractical problems.

Adiabatic Quantum Kitchen Sinks for Learning Kernels Using Randomized Features