Quantum Kitchen Sinks: An algorithm for machine learning on near-term quantum computers

Noisy intermediate-scale quantum (NISQ) computing devices are an exciting platform for the exploration of the power of near-term quantum applications. We describe a near-term quantum application for machine learning tasks by building upon the classical algorithm known as random kitchen sinks. Our technique, called quantum kitchen sinks, uses quantum circuits to nonlinearly transform classical inputs into features that can then be used in a number of machine learning algorithms. We demonstrate the power and flexibility of this proposal by using it to solve binary classification problems for synthetic datasets as well as handwritten digits from the MNIST database. Simulations show, in particular, that small quantum circuits provide significant performance lift over standard linear classical algorithms, reducing classification error rates from 50% to <0.1%, and from 4.1% to 1.4% in these two examples, respectively. We show comparable performance for these examples in experiments with superconducting qubits.