Parallel hybrid quantum-classical machine learning for kernelized time-series classification
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866914682338541568 |
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| author | Baker, Jack S. Park, Gilchan Yu, Kwangmin Ghukasyan, Ara Goktas, Oktay Radha, Santosh Kumar |
| author_facet | Baker, Jack S. Park, Gilchan Yu, Kwangmin Ghukasyan, Ara Goktas, Oktay Radha, Santosh Kumar |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2305_05881 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Parallel hybrid quantum-classical machine learning for kernelized time-series classification Baker, Jack S. Park, Gilchan Yu, Kwangmin Ghukasyan, Ara Goktas, Oktay Radha, Santosh Kumar Quantum Physics Computational Physics 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. |
| title | Parallel hybrid quantum-classical machine learning for kernelized time-series classification |
| topic | Quantum Physics Computational Physics |
| url | https://arxiv.org/abs/2305.05881 |