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Compositional optimization of quantum circuits for quantum kernels of support vector machines

Elham TorabianRoman V. Krems
Mar 2022
摘要
While quantum machine learning (ML) has been proposed to be one of the mostpromising applications of quantum computing, how to build quantum ML modelsthat outperform classical ML remains a major open question. Here, wedemonstrate a Bayesian algorithm for constructing quantum kernels for supportvector machines that adapts quantum gate sequences to data. The algorithmincreases the complexity of quantum circuits incrementally by appending quantumgates selected with Bayesian information criterion as circuit selection metricand Bayesian optimization of the parameters of the locally optimal quantumcircuits identified. The performance of the resulting quantum models forclassification problems with a small number of training points significantlyexceeds that of optimized classical models with conventional kernels.
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