Work

Quantum Sensing and Hamiltonian Learning under Stochastic Parameter Evolution

Year: 2026

Type: preprint

Abstract: 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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2609.39908

Authors: Koor, Kelvin, Rebentrost, Patrick

Institutions: Centre for Quantum Technologies, National University of Singapore

Language: English

Topic: Quantum Computing Algorithms and Architecture

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: National Research Foundation Singapore, Centre for Quantum Technologies

OpenAlex ID: W7215038769

Cites: 24

Cited by:

Related to: 10