Kernel methods are used extensively in classical machine learning, especially in the field of pattern analysis. In this paper, we propose a kernel-based quantum machine learning algorithm that can be implemented on a near-term, intermediate scale quantum device. Our proposal is based on estimating classically intractable kernel functions, using a restricted quantum model known as deterministic quantum computing with one qubit. Our method provides a framework for studying the role of quantum correlations other than quantum entanglement for machine learning applications.
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