Minimal Quantum Reservoirs with Hamiltonian Encoding
Fuente:
arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866916764764340224 |
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| author | McCaul, Gerard Gongora, Juan Sebastian Totero Otieno, Wendy Savelev, Sergey Zagoskin, Alexandre Balanov, Alexander G. |
| author_facet | McCaul, Gerard Gongora, Juan Sebastian Totero Otieno, Wendy Savelev, Sergey Zagoskin, Alexandre Balanov, Alexander G. |
| contents | We investigate a minimal architecture for quantum reservoir computing based on Hamiltonian encoding, in which input data is injected via modulation of system parameters rather than state preparation. This approach circumvents many of the experimental overheads typically associated with quantum machine learning, enabling computation without feedback, memory, or state tomography. We demonstrate that such a minimal quantum reservoir, despite lacking intrinsic memory, can perform nonlinear regression and prediction tasks when augmented with post-processing delay embeddings. Our results provide a conceptually and practically streamlined framework for quantum information processing, offering a clear baseline for future implementations on near-term quantum hardware. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_22575 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Minimal Quantum Reservoirs with Hamiltonian Encoding McCaul, Gerard Gongora, Juan Sebastian Totero Otieno, Wendy Savelev, Sergey Zagoskin, Alexandre Balanov, Alexander G. Quantum Physics We investigate a minimal architecture for quantum reservoir computing based on Hamiltonian encoding, in which input data is injected via modulation of system parameters rather than state preparation. This approach circumvents many of the experimental overheads typically associated with quantum machine learning, enabling computation without feedback, memory, or state tomography. We demonstrate that such a minimal quantum reservoir, despite lacking intrinsic memory, can perform nonlinear regression and prediction tasks when augmented with post-processing delay embeddings. Our results provide a conceptually and practically streamlined framework for quantum information processing, offering a clear baseline for future implementations on near-term quantum hardware. |
| title | Minimal Quantum Reservoirs with Hamiltonian Encoding |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2505.22575 |