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| Hauptverfasser: | , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Online-Zugang: | https://arxiv.org/abs/2505.14450 |
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| _version_ | 1866916746527506432 |
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| author | Sasaki, Daiki Koga, Ryosuke Kuroiwa, Taihei Ito, Yuya Chen, Chih-Chieh Sogabe, Tomah |
| author_facet | Sasaki, Daiki Koga, Ryosuke Kuroiwa, Taihei Ito, Yuya Chen, Chih-Chieh Sogabe, Tomah |
| contents | We propose a Hamiltonian-level framework for non-Markovian quantum reservoir computing directly tailored for analog hardware implementations. By dividing the reservoir into a system block and an environment block and evolving their joint state under a unified Hamiltonian, our architecture naturally embeds memory backflow by harnessing entanglement-induced information backflow with tunable coupling strengths. Numerical benchmarks on short-term memory tasks demonstrate that operating in non-Markovian regimes yields significantly slower memory decay compared to the Markovian limit. Further analyzing the echo-state property (ESP), showing that the non-Markovian quantum reservoir evolves from two different initial states, they do not converge to the same trajectory even after a long time, strongly suggesting that the ESP is effectively violated. Our work provides the first demonstration in quantum reservoir computing that strong non-Markovianity can fundamentally violate the ESP, such that conventional linear-regression readouts fail to deliver stable training and inference. Finally, we experimentally showed that, with an appropriate time-evolution step size, the non-Markovian reservoir exhibits superior performance on higher-order nonlinear autoregressive moving-average(NARMA) tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_14450 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Hamiltonian-Driven Architectures for Non-Markovian Quantum Reservoir Computing Sasaki, Daiki Koga, Ryosuke Kuroiwa, Taihei Ito, Yuya Chen, Chih-Chieh Sogabe, Tomah Quantum Physics We propose a Hamiltonian-level framework for non-Markovian quantum reservoir computing directly tailored for analog hardware implementations. By dividing the reservoir into a system block and an environment block and evolving their joint state under a unified Hamiltonian, our architecture naturally embeds memory backflow by harnessing entanglement-induced information backflow with tunable coupling strengths. Numerical benchmarks on short-term memory tasks demonstrate that operating in non-Markovian regimes yields significantly slower memory decay compared to the Markovian limit. Further analyzing the echo-state property (ESP), showing that the non-Markovian quantum reservoir evolves from two different initial states, they do not converge to the same trajectory even after a long time, strongly suggesting that the ESP is effectively violated. Our work provides the first demonstration in quantum reservoir computing that strong non-Markovianity can fundamentally violate the ESP, such that conventional linear-regression readouts fail to deliver stable training and inference. Finally, we experimentally showed that, with an appropriate time-evolution step size, the non-Markovian reservoir exhibits superior performance on higher-order nonlinear autoregressive moving-average(NARMA) tasks. |
| title | Hamiltonian-Driven Architectures for Non-Markovian Quantum Reservoir Computing |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2505.14450 |