On the capability of a class of quantum sensors

Yu, Qi; Wang, Yuanlong; Dong, Daoyi; Petersen, Ian R.

doi:10.1016/j.automatica.2021.109612

Cited by 10 publications

(6 citation statements)

References 43 publications

(35 reference statements)

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“…For example, although several results on identifiability of quantum systems [98], [113] have been presented, the identifiability of more general quantum systems is not investigated. The identifiability of quantum systems has a close connection to characterizing the capability of quantum sensors and may impose new challenges different from classical system identification problems [113], [128]. Adaptive strategies have only been applied to several simple quantum identification tasks (e.g., estimating the Hamiltonian parameter of a two-level system [92]) and more adaptive algorithms need to be developed to enhance the identification precision for quantum systems.…”

Section: Quantum Parameter Identificationmentioning

confidence: 99%

Quantum estimation, control and learning: opportunities and challenges

Dong¹,

Petersen²

2022

Preprint

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The development of estimation and control theories for quantum systems is a fundamental task for practical quantum technology. This vision article presents a brief introduction to challenging problems and potential opportunities in the emerging areas of quantum estimation, control and learning. The topics cover quantum state estimation, quantum parameter identification, quantum filtering, quantum open-loop control, quantum feedback control, machine learning for estimation and control of quantum systems, and quantum machine learning.

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Section: Quantum Parameter Identificationmentioning

confidence: 99%

Quantum estimation, control and learning: opportunities and challenges

Dong¹,

Petersen²

2022

Preprint

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“…A related problem is the need to create new sensors. Perhaps one of the solutions will be quantum sensors, which can provide high sensitivity and accuracy for measuring key information in quantum or classical physical systems [42,43]. Recent achievements in quantum technologies helped to reach a new level in measuring the parameters of the studied systems.…”

Section: Do We Measure What We Really Need To?mentioning

confidence: 99%

Environmental Justice and the Use of Artificial Intelligence in Urban Air Pollution Monitoring

Krupnova

Rakova

Bondarenko

et al. 2022

BDCC

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The main aims of urban air pollution monitoring are to optimize the interaction between humanity and nature, to combine and integrate environmental databases, and to develop sustainable approaches to the production and the organization of the urban environment. One of the main applications of urban air pollution monitoring is for exposure assessment and public health studies. Artificial intelligence (AI) and machine learning (ML) approaches can be used to build air pollution models to predict pollutant concentrations and assess environmental and health risks. Air pollution data can be uploaded into AI/ML models to estimate different exposure levels within different communities. The correlation between exposure estimates and public health surveys is important for assessing health risks. These aspects are critical when it concerns environmental injustice. Computational approaches should efficiently manage, visualize, and integrate large datasets. Effective data integration and management are a key to the successful application of computational intelligence approaches in ecology. In this paper, we consider some of these constraints and discuss possible ways to overcome current problems and environmental injustice. The most successful global approach is the development of the smart city; however, such an approach can only increase environmental injustice as not all the regions have access to AI/ML technologies. It is challenging to develop successful regional projects for the analysis of environmental data in the current complicated operating conditions, as well as taking into account the time, computing power, and constraints in the context of environmental injustice.

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“…measurement noise. Note that our CLS estimation (10) is in fact equivalent to the least squares estimation of the linear regression problem…”

Section: A Optimality Criterionmentioning

confidence: 99%

“…(i) quantum state tomography (QST) which aims to estimate unknown states [4], [5], [6]; (ii) quantum process tomography which targets in identifying parameters of evolution operators [7], [8], [9], [10] (e.g., the system Hamiltonian [11], [12], [13], [14], [15], [16]); and (iii) quantum detector tomography (QDT) which aims to identify and calibrate quantum measurement devices.…”

Section: Introductionmentioning

confidence: 99%

Optimal and two-step adaptive quantum detector tomography

Xiao¹,

Wang²,

Dong³

et al. 2021

Preprint

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Quantum detector tomography is a fundamental technique for calibrating quantum devices and performing quantum engineering tasks. In this paper, we design optimal probe states for detector estimation based on the minimum upper bound of the mean squared error (UMSE) and the maximum robustness. We establish the minimum UMSE and the minimum condition number for quantum detectors and provide concrete examples that can achieve optimal detector tomography. In order to enhance estimation precision, we also propose a two-step adaptive detector tomography algorithm and investigate how this adaptive strategy can be used to achieve efficient estimation of quantum detectors. Moreover, the superposition of coherent states is used as probe states for quantum detector tomography and the estimation error is analyzed. Numerical results demonstrate the effectiveness of both the proposed optimal and adaptive quantum detector tomography methods.

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On the capability of a class of quantum sensors

Cited by 10 publications

References 43 publications

Quantum estimation, control and learning: opportunities and challenges

Quantum estimation, control and learning: opportunities and challenges

Environmental Justice and the Use of Artificial Intelligence in Urban Air Pollution Monitoring

Optimal and two-step adaptive quantum detector tomography

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