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Efficient interpolation of molecular properties across chemical compound space with low-dimensional descriptors
We demonstrate accurate data-starved models of molecular properties for interpolation in chemical compound spaces with low-dimensional descriptors.
Universal expressiveness of variational quantum classifiers and quantum kernels for support vector machines
Because the PromiseBQP complexity class includes all Bounded-Error Quantum Polynomial-Time (BQP) decision problems, our results imply that there exists a feature map and a quantum kernel that make variational quantum classifiers and quantum kernel support vector machines efficient solvers for any BQP problem.
Compositional optimization of quantum circuits for quantum kernels of support vector machines
While quantum machine learning (ML) has been proposed to be one of the most promising applications of quantum computing, how to build quantum ML models that outperform classical ML remains a major open question.
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