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 the (approximate) degree of the interaction graph. Though Lindbladians present new challenges not present in the special case of Hamiltonians, our algorithm achieves the suite of desiderata attained by state-of-the-art Hamiltonian learning algorithms: (1) it uses non-adaptive, ancilla-free randomized Pauli measurement ci
This research addresses a fundamental challenge in quantum computing by developing an algorithm for characterizing open quantum systems, which are ubiquitous in realistic quantum hardware and notoriously difficult to model.
Accurate characterization of open quantum systems is critical for building robust quantum computers and understanding their behavior, directly impacting the path to fault-tolerant quantum computation and practical quantum applications.
The ability to efficiently learn the structure of Lindbladians significantly advances the toolkit for quantum engineers and researchers, enabling better error mitigation, system design, and performance optimization for quantum devices.
- · Quantum computing researchers
- · Quantum hardware manufacturers
- · Quantum algorithm developers
- · Quantum software companies
- · Classical simulation methods for quantum systems
- · Companies reliant on only closed-system quantum models
Improved understanding and control over quantum decoherence and errors in quantum computing platforms.
Accelerated development of more stable and reliable quantum processors, pushing closer to quantum advantage for specific problems.
Potential for new classes of quantum algorithms that exploit open system dynamics, or more efficient quantum machine learning applications through better system knowledge.
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Read at arXiv cs.LG