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PreprintCC0arXiv

Quantum-Fisher-information-aware optimization yields a quantum reservoir advantage over tuned classical reservoirs

  • Phi Hung Nguyen
  • Daniel Soh
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Abstract

We show that a quantum reservoir computer optimized with its quantum Fisher information (QFI) has a quantum advantage over a classical reservoir of equal readout cost, provided by a quantum-information advantage: optimizing the quantum parameters with the QFI metric. On Mackey-Glass forecasting, a driven Jaynes-Cummings reservoir with one to three atoms beats a tuned echo-state network at every size from 4 to 30 trained weights, by 25-69% in held-out error, and is ahead of 99.5% of 10,000 random networks. After grid and Bayesian searches saturate, the QFI-aware stage lowers the error by 29-51% at 12-30 weights, beyond equal-budget black-box search or classical-parameter gradients alone, and at 30 weights turns a loss into a win. We then derive a fundamental QFI bound on the first-order rate of change of the retrained loss and factorize the gap to this bound into five link efficiencies that identify the physical mechanism limiting optimization.

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Cite

BibTeXRIS
BibTeX
@misc{nguyen2026quantumfisherinformationaware,
  title = {{Quantum-Fisher-information-aware optimization yields a quantum reservoir advantage over tuned classical reservoirs}},
  author = {Phi Hung Nguyen and Daniel Soh},
  howpublished = {arXiv},
  year = {2026},
  month = oct,
  doi = {10.48550/arxiv.2610.14454},
  eprint = {2610.14454},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2610.14454},
  note = {CC0},
}
RIS
TY  - UNPB
TI  - Quantum-Fisher-information-aware optimization yields a quantum reservoir advantage over tuned classical reservoirs
AU  - Phi Hung Nguyen
AU  - Daniel Soh
PY  - 2026
DA  - 2026/10/10
PB  - arXiv
DO  - 10.48550/arxiv.2610.14454
UR  - https://arxiv.org/abs/2610.14454
AB  - We show that a quantum reservoir computer optimized with its quantum Fisher information (QFI) has a quantum advantage over a classical reservoir of equal readout cost, provided by a quantum-information advantage: optimizing the quantum parameters with the QFI metric. On Mackey-Glass forecasting, a driven Jaynes-Cummings reservoir with one to three atoms beats a tuned echo-state network at every size from 4 to 30 trained weights, by 25-69% in held-out error, and is ahead of 99.5% of 10,000 random networks. After grid and Bayesian searches saturate, the QFI-aware stage lowers the error by 29-51% at 12-30 weights, beyond equal-budget black-box search or classical-parameter gradients alone, and at 30 weights turns a loss into a win. We then derive a fundamental QFI bound on the first-order rate of change of the retrained loss and factorize the gap to this bound into five link efficiencies that identify the physical mechanism limiting optimization.
ER  -