Parallel hybrid quantum-classical machine learning for kernelized time-series classification

• URL: http://arxiv.org/abs/2305.05881v2
• Date: Sat, 17 Feb 2024 21:17:37 GMT
• Title: Parallel hybrid quantum-classical machine learning for kernelized time-series classification
• Authors: Jack S. Baker, Gilchan Park, Kwangmin Yu, Ara Ghukasyan, Oktay Goktas and Santosh Kumar Radha
• Abstract summary: We tackle with hybrid quantum-classical machine, deducing temporal temporal between pairwise instances using a time-series Hamiltonian (TSHK) algorithm. Because we treat the kernel weighting step as a differentiable differentiable kernel function, our method can be regarded as an end learnable hybrid quantum-series techniques.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Supervised time-series classification garners widespread interest because of its applicability throughout a broad application domain including finance, astronomy, biosensors, and many others. In this work, we tackle this problem with hybrid quantum-classical machine learning, deducing pairwise temporal relationships between time-series instances using a time-series Hamiltonian kernel (TSHK). A TSHK is constructed with a sum of inner products generated by quantum states evolved using a parameterized time evolution operator. This sum is then optimally weighted using techniques derived from multiple kernel learning. Because we treat the kernel weighting step as a differentiable convex optimization problem, our method can be regarded as an end-to-end learnable hybrid quantum-classical-convex neural network, or QCC-net, whose output is a data set-generalized kernel function suitable for use in any kernelized machine learning technique such as the support vector machine (SVM). Using our TSHK as input to a SVM, we classify univariate and multivariate time-series using quantum circuit simulators and demonstrate the efficient parallel deployment of the algorithm to 127-qubit superconducting quantum processors using quantum multi-programming.

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