Continuous Variable Hamiltonian Learning at Heisenberg Limit via Displacement-Random Unitary Transformation

• URL: http://arxiv.org/abs/2510.08419v1
• Date: Thu, 09 Oct 2025 16:37:47 GMT
• Title: Continuous Variable Hamiltonian Learning at Heisenberg Limit via Displacement-Random Unitary Transformation
• Authors: Xi Huang, Lixing Zhang, Di Luo,
• Abstract summary: Displacement-Random Unitary Transformation (D-RUT) learns the coefficients of general, arbitrary finite-order bosonic Hamiltonians.<n>For multi-mode systems, we develop a hierarchical coefficients recovering strategy with superior statistical efficiency.
• Score: 7.504749065917898
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Characterizing the Hamiltonians of continuous-variable (CV) quantum systems is a fundamental challenge laden with difficulties arising from infinite-dimensional Hilbert spaces and unbounded operators. Existing protocols for achieving the Heisenberg limit precision are often restricted to specific Hamiltonian structures or demand experimentally challenging resources. In this work, we introduce an efficient and experimentally accessible protocol, the Displacement-Random Unitary Transformation (D-RUT), that learns the coefficients of general, arbitrary finite-order bosonic Hamiltonians with a total evolution time scaling as $O(1/\epsilon)$ for a target precision $\epsilon$ robust to SPAM error. For multi-mode systems, we develop a hierarchical coefficients recovering strategy with superior statistical efficiency. Furthermore, we extend our protocol to first quantization, enabling the learning of fundamental physical parameters from Hamiltonians expressed in position and momentum operators at the Heisenberg limit.

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