Heisenberg-limited Hamiltonian learning for interacting bosons

IF 6.6 1区 物理与天体物理 Q1 PHYSICS, APPLIED
Haoya Li, Yu Tong, Tuvia Gefen, Hongkang Ni, Lexing Ying
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引用次数: 0

Abstract

We develop a protocol for learning a class of interacting bosonic Hamiltonians from dynamics with Heisenberg-limited scaling. For Hamiltonians with an underlying bounded-degree graph structure, we can learn all parameters with root mean square error ϵ using \({\mathcal{O}}(1/\epsilon )\) total evolution time, which is independent of the system size, in a way that is robust against state-preparation and measurement error. In the protocol, we only use bosonic coherent states, beam splitters, phase shifters, and homodyne measurements, which are easy to implement on many experimental platforms. A key technique we develop is to apply random unitaries to enforce symmetry in the effective Hamiltonian, which may be of independent interest.

Abstract Image

相互作用玻色子的海森堡有限哈密顿学习
我们开发了一种协议,用于从具有海森堡限制缩放的动力学中学习一类相互作用玻色哈密顿。对于具有底层有界度图结构的哈密顿,我们可以通过使用({\mathcal{O}}(1/\epsilon ))总演化时间,以均方根误差ϵ学习所有参数,这与系统大小无关,而且这种方式对状态准备和测量误差具有鲁棒性。在协议中,我们只使用玻色相干态、分光器、移相器和同调测量,这在许多实验平台上都很容易实现。我们开发的一项关键技术是应用随机单元来加强有效哈密顿的对称性,这可能会引起独立的兴趣。
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来源期刊
npj Quantum Information
npj Quantum Information Computer Science-Computer Science (miscellaneous)
CiteScore
13.70
自引率
3.90%
发文量
130
审稿时长
29 weeks
期刊介绍: The scope of npj Quantum Information spans across all relevant disciplines, fields, approaches and levels and so considers outstanding work ranging from fundamental research to applications and technologies.
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