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Supervised time-series classification garners widespread interest because ofits applicability throughout a broad application domain including finance,astronomy, biosensors, and many others. In this work, we tackle this problemwith hybrid quantum-classical machine learning, deducing pairwise temporalrelationships between time-series instances using a time-series Hamiltoniankernel (TSHK). A TSHK is constructed with a sum of inner products generated byquantum states evolved using a parameterized time evolution operator. This sumis then optimally weighted using techniques derived from multiple kernellearning. Because we treat the kernel weighting step as a differentiable convexoptimization problem, our method can be regarded as an end-to-end learnablehybrid quantum-classical-convex neural network, or QCC-net, whose output is adata set-generalized kernel function suitable for use in any kernelized machinelearning technique such as the support vector machine (SVM). Using our TSHK asinput to a SVM, we classify univariate and multivariate time-series usingquantum circuit simulators and demonstrate the efficient parallel deployment ofthe algorithm to 127-qubit superconducting quantum processors using quantummulti-programming.

Massively parallel hybrid quantum-classical machine learning for kernelized time-series classification