Quantum Sensing and Hamiltonian Learning under Stochastic Parameter Evolution

@misc{koor2026quantum,
  title = {{Quantum Sensing and Hamiltonian Learning under Stochastic Parameter Evolution}},
  author = {Kelvin Koor and Patrick Rebentrost},
  howpublished = {arXiv},
  year = {2026},
  month = sep,
  doi = {10.48550/arxiv.2609.39908},
  eprint = {2609.39908},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2609.39908},
}

TY  - UNPB
TI  - Quantum Sensing and Hamiltonian Learning under Stochastic Parameter Evolution
AU  - Kelvin Koor
AU  - Patrick Rebentrost
PY  - 2026
DA  - 2026/09/30
PB  - arXiv
DO  - 10.48550/arxiv.2609.39908
UR  - https://arxiv.org/abs/2609.39908
AB  - We introduce the notion of stochastically evolving Hamiltonians (SEHs), which are local Hamiltonians whose coefficients are governed by stochastic differential equations (SDEs). After developing some theoretical aspects of these objects, we consider the task of Hamiltonian learning for SEHs. We reanalyse and make necessary extensions to the Huang-Tong-Fang-Su protocol, which first achieved the Heisenberg limit for Hamiltonian coefficient learning, under these circumstances. We delineate theoretically the regimes where the Heisenberg limit can and cannot be retained. Along the way we develop a few tools in quantum sensing and Hamiltonian simulation that may be of independent interest under similar settings in quantum information processing. The broad aim is to encourage the exploration of various settings in quantum information processing in which the control parameters are governed by SDEs.
ER  -