Machine learning time-local generators of open quantum dynamics

Mazza, Paolo P.; Zietlow, Dominik; Carollo, Federico; Andergassen, Sabine; Martius, Georg; Lesanovsky, Igor

doi:10.1103/physrevresearch.3.023084

Cited by 20 publications

(13 citation statements)

References 31 publications

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“…Importantly, we only train our models on this data in this work, since it represents all that is realistically available in experiments. This is in contrast with other work where the training is based on the assumed knowledge of perfect, or slightly noisy, quantum states throughout the evolution [8,[37][38][39]. Although this data can in principle be acquired, it would require impractically many quantum process tomography experiments.…”

Section: B Trainingmentioning

confidence: 99%

Quantum-tailored machine-learning characterization of a superconducting qubit

Genois,

Gross,

Di Paolo

et al. 2021

Preprint

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Machine learning (ML) is a promising approach for performing challenging quantum-information tasks such as device characterization, calibration and control. ML models can train directly on the data produced by a quantum device while remaining agnostic to the quantum nature of the learning task. However, these generic models lack physical interpretability and usually require large datasets in order to learn accurately. Here we incorporate features of quantum mechanics in the design of our ML approach to characterize the dynamics of a quantum device and learn device parameters. This physics-inspired approach outperforms physics-agnostic recurrent neural networks trained on numerically generated and experimental data obtained from continuous weak measurement of a driven superconducting transmon qubit. This demonstration shows how leveraging domain knowledge improves the accuracy and efficiency of this characterization task, thus laying the groundwork for more scalable characterization techniques.

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Section: B Trainingmentioning

confidence: 99%

Quantum-tailored machine-learning characterization of a superconducting qubit

Genois,

Gross,

Di Paolo

et al. 2021

Preprint

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“…This procedure resembles common machine learning techniques for recovering the dynamics of open quantum systems, e.g. [30].…”

Section: Introductionmentioning

confidence: 99%

Recovering models of open quantum systems from data via polynomial optimization: Towards globally convergent quantum system identification

Bondar¹,

Popovych²,

Jacobs³

et al. 2022

Preprint

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Current quantum devices suffer imperfections as a result of fabrication, as well as noise and dissipation as a result of coupling to their immediate environments. Because of this, it is often difficult to obtain accurate models of their dynamics from first principles. An alternative is to extract such models from time-series measurements of their behavior. Here, we formulate this systemidentification problem as a polynomial optimization problem. Recent advances in optimization have provided globally convergent solvers for this class of problems, which using our formulation prove estimates of the Kraus map or the Lindblad equation. We include an overview of the state-of-the-art algorithms, bounds, and convergence rates, and illustrate the use of this approach to modeling open quantum systems.

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“…Recently, machine learning methods have also been applied to obtain effective dynamical generators for local degrees of freedom of a many-body quantum system [13]. The idea is the following: let us assume that one is interested in the time-evolution of a subsystem S of a larger quantum system, as depicted in the sketch in figure 1(a).…”

Section: Introductionmentioning

confidence: 99%

Inferring Markovian quantum master equations of few-body observables in interacting spin chains

et al. 2022

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Full information about a many-body quantum system is usually out-of-reach due to the exponential growth --- with the size of the system --- of the number of parameters needed to encode its state. Nonetheless, in order to understand the complex phenomenology that can be observed in these systems, it is often sufficient to consider dynamical or stationary properties of local observables or, at most, of few-body correlation functions. These quantities are typically studied by singling out a specific subsystem of interest and regarding the remainder of the many-body system as an effective bath. In the simplest scenario, the subsystem dynamics, which is in fact an open quantum dynamics, can be approximated through Markovian quantum master equations. Here, we formulate the problem of finding the generator of the subsystem dynamics as a variational problem, which we solve using the standard toolbox of machine learning for optimization. This dynamical or ``Lindblad" generator provides the relevant dynamical parameters for the subsystem of interest. Importantly, the algorithm we develop is constructed such that the learned generator implements a physically consistent open quantum time-evolution. We exploit this to learn the generator of the dynamics of a subsystem of a many-body system subject to a unitary quantum dynamics. We explore the capability of our method to recover the time-evolution of a two-body subsystem and exploit the physical consistency of the generator to make predictions on the stationary state of the subsystem dynamics.

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Machine learning time-local generators of open quantum dynamics

Cited by 20 publications

References 31 publications

Quantum-tailored machine-learning characterization of a superconducting qubit

Quantum-tailored machine-learning characterization of a superconducting qubit

Recovering models of open quantum systems from data via polynomial optimization: Towards globally convergent quantum system identification

Inferring Markovian quantum master equations of few-body observables in interacting spin chains

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