Machine-Learning-Assisted Quantum Control in a Random Environment

Huang, Tang-You; Ban, Yong-Ling; Sherman, E. Ya.; Chen, Xi

doi:10.1103/physrevapplied.17.024040

Cited by 16 publications

(6 citation statements)

References 51 publications

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“…RL has also been applied for problems such as state control [113], gate control [114,115], for generating controls robust against certain types of errors [116], and for the control of multilevel dissipative quantum systems [117]. QOC with supervised ML [118,119] and with convolutional neural networks trained through deep learning architecture for a quantum particle in a disordered system [120] have also been reported. Another type of ML, namely differential programming (DP), together with a neural network was used for eigenstate preparation in a variety of single and multi-qubit systems [121] as well as for the control of quantum thermal machines [122].…”

Section: E Machine Learning Methodsmentioning

confidence: 99%

Quantum Optimal Control: Practical Aspects and Diverse Methods

Mahesh¹,

Batra²,

Ram³

2022

Preprint

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Quantum controls realize the unitary or nonunitary operations employed in quantum computers, quantum simulators, quantum communications, and other quantum information devices. They implement the desired quantum dynamics with the help of electric, magnetic, or electromagnetic control fields. Quantum optimal control (QOC) deals with designing an optimal control field modulation that most precisely implements a desired quantum operation with minimum energy consumption and maximum robustness against hardware imperfections as well as external noise.Over the last two decades, numerous QOC methods have been proposed. They include asymptotic methods, direct search, gradient methods, variational methods, machine learning methods, etc. In this review, we shall introduce the basic ideas of QOC, discuss practical challenges, and then take an overview of the diverse QOC methods.

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Section: E Machine Learning Methodsmentioning

confidence: 99%

Quantum Optimal Control: Practical Aspects and Diverse Methods

Mahesh¹,

Batra²,

Ram³

2022

Preprint

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“…Note that robust quantum controls with respect to small system uncertainties can also be designed when formulated as a SL task [ 40 , 41 , 42 ]. Convolutional neural networks and SL technique have been used recently to design control protocols in a random environment [ 43 ].…”

Section: Introductionmentioning

confidence: 99%

Characterization of a Driven Two-Level Quantum System by Supervised Learning

Couturier

Dionis

Guérin

et al. 2023

Entropy

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We investigate the extent to which a two-level quantum system subjected to an external time-dependent drive can be characterized by supervised learning. We apply this approach to the case of bang-bang control and the estimation of the offset and the final distance to a given target state. For any control protocol, the goal is to find the mapping between the offset and the distance. This mapping is interpolated using a neural network. The estimate is global in the sense that no a priori knowledge is required on the relation to be determined. Different neural network algorithms are tested on a series of data sets. We show that the mapping can be reproduced with very high precision in the direct case when the offset is known, while obstacles appear in the indirect case starting from the distance to the target. We point out the limits of the estimation procedure with respect to the properties of the mapping to be interpolated. We discuss the physical relevance of the different results.

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“…For example, in [26] a reinforcement learning algorithm has been proposed in order to find an optimal driving protocol for a state transition scheme. Another case is [27], where a supervised learning algorithm classifies randomness of a system in order to find an optimal control policy.…”

Section: Introductionmentioning

confidence: 99%

Predicting the minimum control time of quantum protocols with artificial neural networks

Mirkin

Wisniacki

2023

Quantum Sci. Technol.

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Quantum control relies on the driving of quantum states without the loss of coherence, thus the leakage of quantum properties into the environment over time is a fundamental challenge. One work-around is to implement fast protocols, hence the Minimal Control Time (MCT) is of upmost importance. Here, we employ a machine learning network in order to estimate the MCT in a state transfer protocol. An unsupervised learning approach is considered by using a combination of an autoencoder network with the k-means clustering tool. The Landau-Zener (LZ) Hamiltonian is analyzed given that it has an analytical MCT and a distinctive topology change in the control landscape when the total evolution time is either under or over the MCT. We obtain that the network is able to not only produce an estimation of the MCT but also gains an understanding of the landscape's topologies. Similar results are found for the generalized LZ Hamiltonian while limitations to our very simple architecture were encountered.

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Machine-Learning-Assisted Quantum Control in a Random Environment

Cited by 16 publications

References 51 publications

Quantum Optimal Control: Practical Aspects and Diverse Methods

Quantum Optimal Control: Practical Aspects and Diverse Methods

Characterization of a Driven Two-Level Quantum System by Supervised Learning

Predicting the minimum control time of quantum protocols with artificial neural networks

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