Dissipation-enabled bosonic Hamiltonian learning via new information-propagation bounds
Tim M\"obus, Andreas Bluhm, Matthias C. Caro, Albert H. Werner,, Cambyse Rouz\'e

TL;DR
This paper introduces a new method for learning bosonic Hamiltonians using dissipation and information-propagation bounds, enabling efficient characterization of continuous-variable quantum systems with practical resources.
Contribution
It develops a moment criterion for bosonic Hamiltonian learning, extends finite-dimensional strategies, and introduces a dissipation-based protocol with new Lieb-Robinson bounds for continuous variable systems.
Findings
Efficient Hamiltonian learning with high success probability
Extension of finite-dimensional learning strategies to bosonic systems
Introduction of a new Lieb-Robinson bound for bosonic Hamiltonian evolution
Abstract
Reliable quantum technology requires knowledge of the dynamics governing the underlying system. This problem of characterizing and benchmarking quantum devices or experiments in continuous time is referred to as the Hamiltonian learning problem. In contrast to multi-qubit systems, learning guarantees for the dynamics of bosonic systems have hitherto remained mostly unexplored. For -mode Hamiltonians given as polynomials in annihilation and creation operators with modes arranged on a lattice, we establish a simple moment criterion in terms of the particle number operator which ensures that learning strategies from the finite-dimensional setting extend to the bosonic setting, requiring only coherent states and heterodyne detection on the experimental side. We then propose an enhanced procedure based on added dissipation that even works if the Hamiltonian time evolution violates this…
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Taxonomy
TopicsQuantum Information and Cryptography · Quantum Computing Algorithms and Architecture · Neural Networks and Reservoir Computing
