Learning Arbitrary Lindbladians from Time Evolution

2026-07-30

AI Summary

This work addresses the efficient learning of unknown Markovian open quantum system generators (Lindbladians) from physical time-evolution data, where the generator involves an exponential number of Hamiltonian and dissipative coefficients. The authors propose a two-stage non-adaptive algorithm that requires neither ancillary qubits nor controlled operations. In the first stage, product Pauli eigenstates are prepared and single-qubit Pauli measurements are performed to identify the support of significant terms. The second stage employs random stabilizer state preparation and measurements in random Clifford bases to accurately estimate the coefficients. This approach achieves, for the first time, near-optimal learning of arbitrary Lindbladians, with experimental complexity exceeding the theoretical lower bound only by logarithmic factors: it estimates all coefficients within error ε using Õ(Λ²/ε²) experiments and total evolution time Õ(Λ/ε²), while classical post-processing remains polynomial in cost.

Abstract

We study the problem of learning an unknown Markovian open-system generator from access to its physical time evolution. This generator, called a Lindbladian, contains Hamiltonian and dissipative coefficients indexed by an exponentially large family of possible Pauli terms. We propose an efficient algorithm that learns arbitrary Lindbladians from time evolution under minimal assumptions. For a Lindbladian of dynamical strength at most $Λ$, the algorithm estimates every coefficient to error $ε$ using $\widetilde O(Λ^2/ε^2)$ experiments and $\widetilde O(Λ/ε^2)$ total evolution time, together with polynomial classical running time. The algorithm consists of two nonadaptive, ancilla-free, and control-free stages: 1. The support-learning stage outputs a candidate support of size $\mathrm{poly}(Λ/η)$ that contains every Hamiltonian and dissipative coordinate of magnitude at least $η$, using $\widetilde O(Λ^2/η^2)$ experiments with preparations of product Pauli eigenstates and single-qubit Pauli measurements. 2.The coefficient-learning stage estimates all coefficients in any candidate support of size $M$ to error $ε$, using $\widetilde O(Λ^2\log M/ε^{2})$ experiments with preparations of random stabilizer states and measurements in random Clifford bases. Composing the two stages identifies and estimates every coefficient of an arbitrary Lindbladian in polynomial time. The experiment-count and total-evolution-time scalings match the lower bounds up to logarithmic factors, so the algorithm is nearly optimal for learning arbitrary Lindbladians.

Problem

• Lindbladian
• open quantum system
• quantum process learning
• Markovian dynamics
• quantum tomography

Innovation

• Lindbladian learning
• open quantum systems
• quantum process tomography
• sample complexity
• non-adaptive quantum algorithm