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New algorithm learns structure of open quantum systems

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a novel algorithm for learning the coefficients of an n-qubit Lindbladian, a mathematical model used to describe open quantum systems. This algorithm achieves several key desiderata, including non-adaptive measurements, operation without prior knowledge of the Lindbladian's structure, and support for learning quasi-local and power-law Lindbladians. The method employs an iterative approach focused on Fourier coefficients and identifies unique challenges in open systems termed "confusing" terms, with improved performance when this confusion is limited. The work also presents simpler algorithms for structure learning of Hamiltonians from real-time evolution and efficient algorithms for learning from high-temperature Gibbs states. AI

IMPACT This research advances the understanding and computational methods for complex quantum systems, potentially impacting future quantum computing and simulation.

RANK_REASON The cluster contains an academic paper detailing a new algorithm for a specific scientific problem. [lever_c_demoted from research: ic=2 ai=0.4]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New algorithm learns structure of open quantum systems

COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Laura Lewis, Ewin Tang, John Wright ·

    Learning the structure of open quantum systems

    arXiv:2606.30358v1 Announce Type: cross Abstract: We design an algorithm for learning the coefficients of an $n$-qubit constant-local Lindbladian to $\varepsilon$ error with $O(g d^2 \log(n) / \varepsilon^2)$ total evolution time, where $g$ is the single-site energy and $d$ is th…

  2. arXiv cs.LG TIER_1 English(EN) · John Wright ·

    Learning the structure of open quantum systems

    We design an algorithm for learning the coefficients of an $n$-qubit constant-local Lindbladian to $\varepsilon$ error with $O(g d^2 \log(n) / \varepsilon^2)$ total evolution time, where $g$ is the single-site energy and $d$ is the (approximate) degree of the interaction graph. T…

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