Multiclass classification of dephasing channels

Palmieri, Adriano Macarone; Federico, B.; Paris, Matteo G. A.; Benedetti, Claudia

doi:10.1103/physreva.104.052412

Cited by 9 publications

(5 citation statements)

References 56 publications

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“…Akram Youssry, 1 Yang Yang, 1 Robert J. Chapman, 1, 2 Ben Haylock, 3,4 Mirko Lobino, 3,5,6 and Alberto Peruzzo 1…”

Section: Supplementary Materials For Experimental Graybox Quantum Con...mentioning

confidence: 99%

“…However, this approach, referred to as "blackbox" (BB), does not provide any information about the underlying physics of the system and cannot be used to study any quantity beyond the output of the model, a major limitation in both classical and quantum control. Nonetheless, it has been used in many applications such as quantifying Non-Markovianity in quantum systems [2], characterizing qubits and environments [3][4][5][6], quantum control [7][8][9], quantum error correction [10,11], optimization of experimental quantum measurements [12,13], and calibration of quantum devices [14]. Reinforcement learning methods such as [15][16][17] have also been explored for the purposes of identification and control of quantum systems.…”

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confidence: 99%

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Experimental graybox quantum control

Youssry¹,

Yang²,

Chapman³

et al. 2022

Preprint

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Understanding and controlling engineered quantum systems is key to developing practical quantum technology. However, given the current technological limitations, such as fabrication imperfections and environmental noise, this is not always possible. To address these issues, a great deal of theoretical and numerical methods for quantum system identification and control have been developed. These methods range from traditional curve fittings, which are limited by the accuracy of the model that describes the system, to machine learning methods, including neural networks, which provide efficient control solutions but do not provide control beyond the output of the model, nor insights into the underlying physical process. Here we experimentally demonstrate a "graybox" approach for characterizing and controlling quantum devices. We report superior performance over model fitting, while generating unitaries and Hamiltonians which are inaccessible with machine learning techniques. Our approach combines prior physics knowledge with high accuracy machine learning and is effective with any problem where the required controlled quantities are not directly accessible from the training set. This method naturally extends to time-dependent and open quantum systems, with applications in quantum noise spectroscopy and cancellation.

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“…Akram Youssry, 1 Yang Yang, 1 Robert J. Chapman, 1, 2 Ben Haylock, 3,4 Mirko Lobino, 3,5,6 and Alberto Peruzzo 1…”

Section: Supplementary Materials For Experimental Graybox Quantum Con...mentioning

confidence: 99%

mentioning

confidence: 99%

Experimental graybox quantum control

Youssry¹,

Yang²,

Chapman³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…More pertinent to the matter at hand, aspects of the problem of distinguishing between Ohmic, sub-Ohmic and super-Ohmic SDs have already been studied: in [35], a scenario where a probe qubit is used to access a second inaccessible one is proposed to infer the Ohmicity class by using NNs and leveraging the special features of quantum synchronization. In [36], a different use of NNs was put forward as tomographic data at just two instants of time were used, rather than a time-series approach. In contrast, this work takes a simpler approach by utilizing the time evolution of a system observable for classification without the need for a probe system or tomographically complete information.…”

Section: Introductionmentioning

confidence: 99%

Spectral density classification for environment spectroscopy

Barr,

Zicari,

Ferraro

et al. 2024

Mach. Learn.: Sci. Technol.

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Spectral densities encode the relevant information characterising the system-environment interaction in an open-quantum system problem. Such information is key to determining the system’s dynamics. In this work, we leverage the potential of machine learning techniques to reconstruct the features of the environment. Specifically, we show that the time evolution of a system observable can be used by an artificial neural network to infer the main features of the spectral density. In particular, for relevant examples of spin-boson models, we can classify with high accuracy the Ohmicity parameter of the environment as either Ohmic, sub-Ohmic or super-Ohmic, thereby distinguishing between different forms of dissipation.

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“…[4][5][6] ML techniques have been proven to be of substantial aid when adapted to quantum states characterization, [7][8][9][10][11][12][13][14] optimization of control strategies, [15][16][17][18] quantum state transport 19,20 as well as for parameter estimation and classification tasks. [21][22][23][24][25][26][27][28] Concerning the characterization of quantum processes, Hamiltonian learning strategies have been extensively investigated in order to provide a reliable solution to this challenge. [29][30][31][32][33][34] Moreover, it is pivotal that the required information is often not directly accessible and must be inferred starting from experimental quantities.…”

Section: Introductionmentioning

confidence: 99%

Multiparameter estimation of continuous-time quantum walk Hamiltonians through machine learning

Gianani

Benedetti

2023

AVS Quantum Science

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The characterization of the Hamiltonian parameters defining a quantum walk is of paramount importance when performing a variety of tasks, from quantum communication to computation. When dealing with physical implementations of quantum walks, the parameters themselves may not be directly accessible, and, thus, it is necessary to find alternative estimation strategies exploiting other observables. Here, we perform the multiparameter estimation of the Hamiltonian parameters characterizing a continuous-time quantum walk over a line graph with n-neighbor interactions using a deep neural network model fed with experimental probabilities at a given evolution time. We compare our results with the bounds derived from estimation theory and find that the neural network acts as a nearly optimal estimator both when the estimation of two or three parameters is performed.

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Multiclass classification of dephasing channels

Cited by 9 publications

References 56 publications

Experimental graybox quantum control

Experimental graybox quantum control

Spectral density classification for environment spectroscopy

Multiparameter estimation of continuous-time quantum walk Hamiltonians through machine learning

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