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Journal articles on the topic 'Quantum sensing'

Author: Grafiati

Published: 4 June 2021

Last updated: 10 March 2023

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1

Sherbert, Kyle M., Naveed Naimipour, Haleh Safavi, Harry C. Shaw, and Mojtaba Soltanalian. "Quantum Compressive Sensing: Mathematical Machinery, Quantum Algorithms, and Quantum Circuitry." Applied Sciences 12, no. 15 (July 26, 2022): 7525. http://dx.doi.org/10.3390/app12157525.

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Compressive sensing is a sensing protocol that facilitates the reconstruction of large signals from relatively few measurements by exploiting known structures of signals of interest, typically manifested as signal sparsity. Compressive sensing’s vast repertoire of applications in areas such as communications and image reconstruction stems from the traditional approach of utilizing non-linear optimization to exploit the sparsity assumption by selecting the lowest-weight (i.e., maximum sparsity) signal consistent with all acquired measurements. Recent efforts in the literature consider instead a data-driven approach, training tensor networks to learn the structure of signals of interest. The trained tensor network is updated to “project” its state onto one consistent with the measurements taken, and is then sampled site by site to “guess” the original signal. In this paper, we take advantage of this computing protocol by formulating an alternative “quantum” protocol, in which the state of the tensor network is a quantum state over a set of entangled qubits. Accordingly, we present the associated algorithms and quantum circuits required to implement the training, projection, and sampling steps on a quantum computer. We supplement our theoretical results by simulating the proposed circuits with a small, qualitative model of LIDAR imaging of earth forests. Our results indicate that a quantum, data-driven approach to compressive sensing may have significant promise as quantum technology continues to make new leaps.

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2

Osborne, Ian S. "Quantum enhanced sensing." Science 373, no. 6555 (August 5, 2021): 637.5–638. http://dx.doi.org/10.1126/science.373.6555.637-e.

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3

Geurdes, Johannes F. "Quantum Remote Sensing." Physics Essays 11, no. 3 (September 1998): 367–74. http://dx.doi.org/10.4006/1.3025312.

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4

Kutas, Mirco, Björn Haase, Patricia Bickert, Felix Riexinger, Daniel Molter, and Georg von Freymann. "Terahertz quantum sensing." Science Advances 6, no. 11 (March 2020): eaaz8065. http://dx.doi.org/10.1126/sciadv.aaz8065.

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Quantum sensing is highly attractive for accessing spectral regions in which the detection of photons is technically challenging: Sample information is gained in the spectral region of interest and transferred via biphoton correlations into another spectral range, for which highly sensitive detectors are available. This is especially beneficial for terahertz radiation, where no semiconductor detectors are available and coherent detection schemes or cryogenically cooled bolometers have to be used. Here, we report on the first demonstration of quantum sensing in the terahertz frequency range in which the terahertz photons interact with a sample in free space and information about the sample thickness is obtained by the detection of visible photons. As a first demonstration, we show layer thickness measurements with terahertz photons based on biphoton interference. As nondestructive layer thickness measurements are of high industrial relevance, our experiments might be seen as a first step toward industrial quantum sensing applications.

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5

Osborne, Ian S. "Enhancing quantum sensing." Science 356, no. 6340 (May 25, 2017): 816.3–816. http://dx.doi.org/10.1126/science.356.6340.816-c.

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6

Farooq, Ahmad, Uman Khalid, Junaid ur Rehman, and Hyundong Shin. "Robust Quantum State Tomography Method for Quantum Sensing." Sensors 22, no. 7 (March 30, 2022): 2669. http://dx.doi.org/10.3390/s22072669.

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Reliable and efficient reconstruction of pure quantum states under the processing of noisy measurement data is a vital tool in fundamental and applied quantum information sciences owing to communication, sensing, and computing. Specifically, the purity of such reconstructed quantum systems is crucial in surpassing the classical shot-noise limit and achieving the Heisenberg limit, regarding the achievable precision in quantum sensing. However, the noisy reconstruction of such resourceful sensing probes limits the quantum advantage in precise quantum sensing. For this, we formulate a pure quantum state reconstruction method through eigenvalue decomposition. We show that the proposed method is robust against the depolarizing noise; it remains unaffected under high strength white noise and achieves quantum state reconstruction accuracy similar to the noiseless case.

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7

Coleman, Hannah, and Matt Brookes. "Quantum sensing the brain." Physics World 34, no. 2 (May 1, 2021): 23–27. http://dx.doi.org/10.1088/2058-7058/34/02/27.

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8

Bakhshandeh, Sadra. "Quantum sensing goes bio." Nature Reviews Materials 7, no. 4 (March 22, 2022): 254. http://dx.doi.org/10.1038/s41578-022-00435-y.

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9

Mitchell, Morgan W. "Number-unconstrained quantum sensing." Quantum Science and Technology 2, no. 4 (August 17, 2017): 044005. http://dx.doi.org/10.1088/2058-9565/aa80c0.

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10

Kongsuwan, Nuttawut, Xiao Xiong, Ping Bai, Jia-Bin You, Ching Eng Png, Lin Wu, and Ortwin Hess. "Quantum Plasmonic Immunoassay Sensing." Nano Letters 19, no. 9 (July 29, 2019): 5853–61. http://dx.doi.org/10.1021/acs.nanolett.9b01137.

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11

Dowran, Mohammadjavad, Ashok Kumar, Benjamin J. Lawrie, Raphael C. Pooser, and Alberto M. Marino. "Quantum-enhanced plasmonic sensing." Optica 5, no. 5 (May 16, 2018): 628. http://dx.doi.org/10.1364/optica.5.000628.

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12

Bonalda, Daniele, Luigi Seveso, and Matteo G. A. Paris. "Quantum Sensing of Curvature." International Journal of Theoretical Physics 58, no. 9 (July 3, 2019): 2914–35. http://dx.doi.org/10.1007/s10773-019-04174-9.

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13

Ajoy, Ashok, Yi-Xiang Liu, Kasturi Saha, Luca Marseglia, Jean-Christophe Jaskula, Ulf Bissbort, and Paola Cappellaro. "Quantum interpolation for high-resolution sensing." Proceedings of the National Academy of Sciences 114, no. 9 (February 14, 2017): 2149–53. http://dx.doi.org/10.1073/pnas.1610835114.

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Recent advances in engineering and control of nanoscale quantum sensors have opened new paradigms in precision metrology. Unfortunately, hardware restrictions often limit the sensor performance. In nanoscale magnetic resonance probes, for instance, finite sampling times greatly limit the achievable sensitivity and spectral resolution. Here we introduce a technique for coherent quantum interpolation that can overcome these problems. Using a quantum sensor associated with the nitrogen vacancy center in diamond, we experimentally demonstrate that quantum interpolation can achieve spectroscopy of classical magnetic fields and individual quantum spins with orders of magnitude finer frequency resolution than conventionally possible. Not only is quantum interpolation an enabling technique to extract structural and chemical information from single biomolecules, but it can be directly applied to other quantum systems for superresolution quantum spectroscopy.

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14

Allen, Michael, Laura Hiscott, Margaret Harris, and Michael Banks. "Sensing gravity, the quantum way." Physics World 34, no. 12 (December 1, 2021): 44–47. http://dx.doi.org/10.1088/2058-7058/34/12/38.

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Devices that exploit the extreme sensitivity of quantum states are making their way out of the lab and into everything from construction and healthcare to seismology. Michael Allen learns more about the technology that goes into building a quantum gravity sensor, and its multitude of uses in research and industry.

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15

Xavier, Jolly, Deshui Yu, Callum Jones, Ekaterina Zossimova, and Frank Vollmer. "Quantum nanophotonic and nanoplasmonic sensing: towards quantum optical bioscience laboratories on chip." Nanophotonics 10, no. 5 (March 1, 2021): 1387–435. http://dx.doi.org/10.1515/nanoph-2020-0593.

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Abstract Quantum-enhanced sensing and metrology pave the way for promising routes to fulfil the present day fundamental and technological demands for integrated chips which surpass the classical functional and measurement limits. The most precise measurements of optical properties such as phase or intensity require quantum optical measurement schemes. These non-classical measurements exploit phenomena such as entanglement and squeezing of optical probe states. They are also subject to lower detection limits as compared to classical photodetection schemes. Biosensing with non-classical light sources of entangled photons or squeezed light holds the key for realizing quantum optical bioscience laboratories which could be integrated on chip. Single-molecule sensing with such non-classical sources of light would be a forerunner to attaining the smallest uncertainty and the highest information per photon number. This demands an integrated non-classical sensing approach which would combine the subtle non-deterministic measurement techniques of quantum optics with the device-level integration capabilities attained through nanophotonics as well as nanoplasmonics. In this back drop, we review the underlining principles in quantum sensing, the quantum optical probes and protocols as well as state-of-the-art building blocks in quantum optical sensing. We further explore the recent developments in quantum photonic/plasmonic sensing and imaging together with the potential of combining them with burgeoning field of coupled cavity integrated optoplasmonic biosensing platforms.

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16

Migliore, Agostino, Ron Naaman, and David N. Beratan. "Sensing of molecules using quantum dynamics." Proceedings of the National Academy of Sciences 112, no. 19 (April 24, 2015): E2419—E2428. http://dx.doi.org/10.1073/pnas.1502000112.

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We design sensors where information is transferred between the sensing event and the actuator via quantum relaxation processes, through distances of a few nanometers. We thus explore the possibility of sensing using intrinsically quantum mechanical phenomena that are also at play in photobiology, bioenergetics, and information processing. Specifically, we analyze schemes for sensing based on charge transfer and polarization (electronic relaxation) processes. These devices can have surprising properties. Their sensitivity can increase with increasing separation between the sites of sensing (the receptor) and the actuator (often a solid-state substrate). This counterintuitive response and other quantum features give these devices favorable characteristics, such as enhanced sensitivity and selectivity. Using coherent phenomena at the core of molecular sensing presents technical challenges but also suggests appealing schemes for molecular sensing and information transfer in supramolecular structures.

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17

Moore, Sean W., and Jacob A. Dunningham. "Secure quantum remote sensing without entanglement." AVS Quantum Science 5, no. 1 (March 2023): 014406. http://dx.doi.org/10.1116/5.0137260.

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Quantum metrology and quantum communications are typically considered as distinct applications in the broader portfolio of quantum technologies. However, there are cases where we might want to combine the two, and recent proposals have shown how this might be achieved in entanglement-based systems. Here, we present an entanglement-free alternative that has advantages in terms of simplicity and practicality, requiring only individual qubits to be transmitted. We demonstrate the performance of the scheme in both the low and high data limits, showing quantum advantages both in terms of measurement precision and security against a range of possible attacks.

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18

Zhao, Wen, Xuan Tang, Xueshi Guo, Xiaoying Li, and Z. Y. Ou. "Quantum entangled Sagnac interferometer." Applied Physics Letters 122, no. 6 (February 6, 2023): 064003. http://dx.doi.org/10.1063/5.0135084.

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A SU(1,1) interferometer (SUI) is a kind of quantum entangled interferometer that uses directly entangled quantum fields for sensing phase change. For rotational sensing, Sagnac geometry is usually adopted. However, because SUI depends on the phase sum of the two arms, traditional Sagnac geometry, when applied to SUI, will result in null signal. In this paper, we modify the traditional Sagnac interferometer by nesting SUIs inside. We show that the rotational signal comes from two parts labeled as “classical” and “quantum,” respectively, and the quantum part, where quantum entangled fields are used for sensing, has the rotational signal enhanced by a factor related to the gain of the SUI.

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19

KAWASAKI, Hideya. "Quantum Dot-based Fluorescent Sensing." Analytical Sciences 33, no. 9 (2017): 987–88. http://dx.doi.org/10.2116/analsci.33.987.

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20

Stray, Ben, Andrew Lamb, Aisha Kaushik, Jamie Vovrosh, Anthony Rodgers, Jonathan Winch, Farzad Hayati, et al. "Quantum sensing for gravity cartography." Nature 602, no. 7898 (February 23, 2022): 590–94. http://dx.doi.org/10.1038/s41586-021-04315-3.

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AbstractThe sensing of gravity has emerged as a tool in geophysics applications such as engineering and climate research1–3, including the monitoring of temporal variations in aquifers4 and geodesy5. However, it is impractical to use gravity cartography to resolve metre-scale underground features because of the long measurement times needed for the removal of vibrational noise6. Here we overcome this limitation by realizing a practical quantum gravity gradient sensor. Our design suppresses the effects of micro-seismic and laser noise, thermal and magnetic field variations, and instrument tilt. The instrument achieves a statistical uncertainty of 20 E (1 E = 10−9 s−2) and is used to perform a 0.5-metre-spatial-resolution survey across an 8.5-metre-long line, detecting a 2-metre tunnel with a signal-to-noise ratio of 8. Using a Bayesian inference method, we determine the centre to ±0.19 metres horizontally and the centre depth as (1.89 −0.59/+2.3) metres. The removal of vibrational noise enables improvements in instrument performance to directly translate into reduced measurement time in mapping. The sensor parameters are compatible with applications in mapping aquifers and evaluating impacts on the water table7, archaeology8–11, determination of soil properties12 and water content13, and reducing the risk of unforeseen ground conditions in the construction of critical energy, transport and utilities infrastructure14, providing a new window into the underground.

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21

Waasem, Niklas, Helmut Fedder, and Patrick Maletinsky. "Technology leaps in quantum sensing." PhotonicsViews 18, no. 4 (August 2021): 36–39. http://dx.doi.org/10.1002/phvs.202100048.

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22

Pirandola, S., B. R. Bardhan, T. Gehring, C. Weedbrook, and S. Lloyd. "Advances in photonic quantum sensing." Nature Photonics 12, no. 12 (November 28, 2018): 724–33. http://dx.doi.org/10.1038/s41566-018-0301-6.

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23

Baker, Christopher G., and Warwick P. Bowen. "Sensing past the quantum limit." Nature 547, no. 7662 (July 2017): 164–65. http://dx.doi.org/10.1038/547164a.

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24

Bin, Shang-Wu, Xin-You Lü, Tai-Shuang Yin, Gui-Lei Zhu, Qian Bin, and Ying Wu. "Mass sensing by quantum criticality." Optics Letters 44, no. 3 (January 25, 2019): 630. http://dx.doi.org/10.1364/ol.44.000630.

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25

Lawrie, B. J., P. D. Lett, A. M. Marino, and R. C. Pooser. "Quantum Sensing with Squeezed Light." ACS Photonics 6, no. 6 (May 13, 2019): 1307–18. http://dx.doi.org/10.1021/acsphotonics.9b00250.

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26

Callan, John F., A. P. Silva, R. C. Mulrooney, and B. Mc Caughan. "Luminescent Sensing with Quantum Dots." Journal of Inclusion Phenomena and Macrocyclic Chemistry 58, no. 3-4 (November 23, 2006): 257–62. http://dx.doi.org/10.1007/s10847-006-9152-8.

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27

Plakhotnik, Taras. "Diamonds for quantum nano sensing." Current Opinion in Solid State and Materials Science 21, no. 1 (February 2017): 25–34. http://dx.doi.org/10.1016/j.cossms.2016.08.001.

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28

Majumdar, Sarangam, and Sukla Pal. "Quorum sensing: a quantum perspective." Journal of Cell Communication and Signaling 10, no. 3 (August 31, 2016): 173–75. http://dx.doi.org/10.1007/s12079-016-0348-4.

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29

Richardson, Logan, Adam Hines, Andrew Schaffer, Brian P. Anderson, and Felipe Guzman. "Quantum hybrid optomechanical inertial sensing." Applied Optics 59, no. 22 (June 30, 2020): G160. http://dx.doi.org/10.1364/ao.393060.

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30

Dzurak, Andrew. "Silicon integration for quantum sensing." Nature Electronics 2, no. 7 (July 2019): 266–67. http://dx.doi.org/10.1038/s41928-019-0278-2.

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31

Liu, Gang-Qin. "Diamond spin quantum sensing under extreme conditions." Acta Physica Sinica 71, no. 6 (2022): 066101. http://dx.doi.org/10.7498/aps.71.20212072.

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Extreme conditions, such as ultra-low temperatures, high pressures, and strong magnetic fields, are critical to producing and studying exotic states of matter. To measure physical properties under extreme conditions, the advanced sensing schemes are required. As a promising quantum sensor, the diamond nitrogen-vacancy (NV) center can detect magnetic field, electronic field, pressure, and temperature with high sensitivity. Considering its nanoscale spatial resolution and ultra-wide working range, the diamond quantum sensing can play an important role in frontier studies involving extreme conditions. This paper reviews the spin and optical properties of diamond NV center under extreme conditions, including low temperature, high temperature, zero field, strong magnetic fields, and high pressures. The opportunities and challenges of diamond quantum sensing under extreme conditions are discussed. The basic knowledge of spin-based quantum sensing and its applications under extreme conditions are also covered.

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32

Ganguly, Kaushik. "Quantum AI: Deep Learning optimization using Hybrid Quantum Filters." International Journal for Research in Applied Science and Engineering Technology 10, no. 9 (September 30, 2022): 1720–33. http://dx.doi.org/10.22214/ijraset.2022.46914.

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Abstract: Deep learning algorithms have shown promising results for different image processing tasks, particularly in remote sensing & image recognition. Till now many studies have been carried out on image processing, which brings a new paradigm of innovative capabilities under the umbrella of intelligent remote sensing and computer vision. Accordingly, quantum processing algorithms have proved to efficiently solve some issues that are undetectable to classical algorithms and processors. Keeping that in mind, a Quantum Convolutional Neural Network (QCNN) architecture along with Hybrid Quantum filters would be utilized supported by cloud computing infrastructures and data centers to provide a broad range of complex AI services and high data availability. This research summaries the conventional techniques of Classical and Quantum Deep Learning and it’s research progress on realworld problems in remote sensing image processing as a comparative demonstration. Last but not least, we evaluate our system by training on Street View House Numbers datasets in order to highlight the feasibility and effectiveness of using Quantum Deep Learning approach in image recognition and other similar applications. Upcoming challenges and future research areas on this spectrum are also discussed.

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33

Maluleke, Rodney, and Oluwatobi Samuel Oluwafemi. "Synthetic Approaches, Modification Strategies and the Application of Quantum Dots in the Sensing of Priority Pollutants." Applied Sciences 11, no. 24 (December 7, 2021): 11580. http://dx.doi.org/10.3390/app112411580.

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Polycyclic aromatic hydrocarbons (PAHs) and nitro-aromatic compounds (NACs) are two classifications of environmental pollutants that have become a source of health concerns. As a result, there have been several efforts towards the development of analytical methods that are efficient and affordable that can sense these pollutants. In recent decades, a wide range of techniques has been developed for the detection of pollutants present in the environment. Among these different techniques, the use of semiconductor nanomaterials, also known as quantum dots, has continued to gain more attention in sensing because of the optical properties that make them useful in the identification and differentiation of pollutants in water bodies. Reported studies have shown great improvement in the sensing of these pollutants. This review article starts with an introduction on two types of organic pollutants, namely polycyclic aromatic hydrocarbons and nitro-aromatic explosives. This is then followed by different quantum dots used in sensing applications. Then, a detailed discussion on different groups of quantum dots, such as carbon-based quantum dots, binary and ternary quantum dots and quantum dot composites, and their application in the sensing of organic pollutants is presented. Different studies on the comparison of water-soluble quantum dots and organic-soluble quantum dots of a fluorescence sensing mechanism are reviewed. Then, different approaches on the improvement of their sensitivity and selectivity in addition to challenges associated with some of these approaches are also discussed. The review is concluded by looking at different mechanisms in the sensing of polycyclic aromatic hydrocarbons and nitro-aromatic compounds.

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34

Jorge, Pedro, Manuel Martins, Tito Trindade, José Santos, and Faramarz Farahi. "Optical Fiber Sensing Using Quantum Dots." Sensors 7, no. 12 (December 21, 2007): 3489–534. http://dx.doi.org/10.3390/s7123489.

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35

Yu, Chung-Jui, Stephen von Kugelgen, Daniel W. Laorenza, and Danna E. Freedman. "A Molecular Approach to Quantum Sensing." ACS Central Science 7, no. 5 (April 20, 2021): 712–23. http://dx.doi.org/10.1021/acscentsci.0c00737.

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36

S W, Bi, Jaffrès H, and Roychoudhuri C S. "Quantum Remote Sensing: Review and Perspective." Journal of Global Change Data & Discovery 3, no. 4 (2019): 317–25. http://dx.doi.org/10.3974/geodp.2019.04.02.

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37

Steffens, A., C. A. Riofrío, W. McCutcheon, I. Roth, B. A. Bell, A. McMillan, M. S. Tame, J. G. Rarity, and J. Eisert. "Experimentally exploring compressed sensing quantum tomography." Quantum Science and Technology 2, no. 2 (May 11, 2017): 025005. http://dx.doi.org/10.1088/2058-9565/aa6ae2.

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38

Chern, Margaret, Joshua C. Kays, Shashi Bhuckory, and Allison M. Dennis. "Sensing with photoluminescent semiconductor quantum dots." Methods and Applications in Fluorescence 7, no. 1 (January 24, 2019): 012005. http://dx.doi.org/10.1088/2050-6120/aaf6f8.

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39

Troiani, F., A. Ghirri, M. G. A. Paris, C. Bonizzoni, and M. Affronte. "Towards quantum sensing with molecular spins." Journal of Magnetism and Magnetic Materials 491 (December 2019): 165534. http://dx.doi.org/10.1016/j.jmmm.2019.165534.

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40

Lee, Joong-Sung, Seung-Jin Yoon, Hyungju Rah, Mark Tame, Carsten Rockstuhl, Seok Ho Song, Changhyoup Lee, and Kwang-Geol Lee. "Quantum plasmonic sensing using single photons." Optics Express 26, no. 22 (October 25, 2018): 29272. http://dx.doi.org/10.1364/oe.26.029272.

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41

Müller, Matthias M., Stefano Gherardini, Nicola Dalla Pozza, and Filippo Caruso. "Noise sensing via stochastic quantum Zeno." Physics Letters A 384, no. 13 (May 2020): 126244. http://dx.doi.org/10.1016/j.physleta.2020.126244.

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42

Gilbert, G., and Y. S. Weinstein. "Aspects of practical remote quantum sensing." Journal of Modern Optics 55, no. 19-20 (November 10, 2008): 3283–91. http://dx.doi.org/10.1080/09500340802428314.

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43

Boss, J. M., K. S. Cujia, J. Zopes, and C. L. Degen. "Quantum sensing with arbitrary frequency resolution." Science 356, no. 6340 (May 25, 2017): 837–40. http://dx.doi.org/10.1126/science.aam7009.

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44

Li, Bei-Bei, Lingfeng Ou, Yuechen Lei, and Yong-Chun Liu. "Cavity optomechanical sensing." Nanophotonics 10, no. 11 (August 24, 2021): 2799–832. http://dx.doi.org/10.1515/nanoph-2021-0256.

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Abstract Cavity optomechanical systems enable interactions between light and mechanical resonators, providing a platform both for fundamental physics of macroscopic quantum systems and for practical applications of precision sensing. The resonant enhancement of both mechanical and optical response in the cavity optomechanical systems has enabled precision sensing of multiple physical quantities, including displacements, masses, forces, accelerations, magnetic fields, and ultrasounds. In this article, we review the progress of precision sensing applications using cavity optomechanical systems. The review is organized in the following way: first we will introduce the physical principles of optomechanical sensing, including a discussion of the noises and sensitivity of the systems, and then review the progress in displacement sensing, mass sensing, force sensing, atomic force microscope (AFM) and magnetic resonance force microscope (MRFM), accelerometry, magnetometry, and ultrasound sensing, and introduce the progress of using quantum techniques especially squeezed light to enhance the performance of the optomechanical sensors. Finally, we give a summary and outlook.

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45

Dong, Yang, Haobin Lin, Wei Zhu, and Fangwen Sun. "High-sensitivity double-quantum magnetometry in diamond via quantum control." JUSTC 52, no. 3 (2022): 3. http://dx.doi.org/10.52396/justc-2021-0249.

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High-fidelity quantum operation of qubits plays an important role in magnetometry based on nitrogen-vacancy (NV) centers in diamonds. However, the nontrivial spin-spin coupling of the NV center decreases signal contrast and sensitivity. Here, we overcome this limitation by exploiting the amplitude modulation of microwaves, which allows us to perfectly detect magnetic signals at low fields. Compared with the traditional double-quantum sensing protocol, the full contrast of the detection signal was recovered, and the sensitivity was enhanced three times in the experiment. Our method is applicable to a wide range of sensing tasks, such as temperature, strain, and electric field.

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Tan, Yayin, Xinhao Hu, Yong Hou, and Zhiqin Chu. "Emerging Diamond Quantum Sensing in Bio-Membranes." Membranes 12, no. 10 (September 30, 2022): 957. http://dx.doi.org/10.3390/membranes12100957.

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Bio-membranes exhibit complex but unique mechanical properties as communicative regulators in various physiological and pathological processes. Exposed to a dynamic micro-environment, bio-membranes can be seen as an intricate and delicate system. The systematical modeling and detection of their local physical properties are often difficult to achieve, both quantitatively and precisely. The recent emerging diamonds hosting quantum defects (i.e., nitrogen-vacancy (NV) center) demonstrate intriguing optical and spin properties, together with their outstanding photostability and biocompatibility, rendering them ideal candidates for biological applications. Notably, the extraordinary spin-based sensing enable the measurements of localized nanoscale physical quantities such as magnetic fields, electrical fields, temperature, and strain. These nanoscale signals can be optically read out precisely by simple optical microscopy systems. Given these exclusive properties, NV-center-based quantum sensors can be widely applied in exploring bio-membrane-related features and the communicative chemical reaction processes. This review mainly focuses on NV-based quantum sensing in bio-membrane fields. The attempts of applying NV-based quantum sensors in bio-membranes to investigate diverse physical and chemical events such as membrane elasticity, phase change, nanoscale bio-physical signals, and free radical formation are fully overviewed. We also discuss the challenges and future directions of this novel technology to be utilized in bio-membranes.

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Meng, Fangyuan, Hongyan Yu, Xuliang Zhou, Yajie Li, Mengqi Wang, Wenyu Yang, Weixi Chen, Yejin Zhang, and Jiaoqing Pan. "Quantum wells micro-ring resonator laser emitting at 1746 nm for gas sensing." Chinese Optics Letters 19, no. 4 (2021): 041406. http://dx.doi.org/10.3788/col202119.041406.

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48

Dong, Yongqiang, Jianhua Cai, Xu You, and Yuwu Chi. "Sensing applications of luminescent carbon based dots." Analyst 140, no. 22 (2015): 7468–86. http://dx.doi.org/10.1039/c5an01487e.

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Carbon based dots (CDs) including carbon quantum dots and graphene quantum dots exhibit unique luminescence properties, such as photoluminescence (PL), chemiluminescence (CL) and electrochemiluminescence (ECL).

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Yang, F., G. Q. Zhou, J. R. Xiao, Q. Li, B. Jia, H. Y. Wang, and J. Gao. "MULTISPECTRAL REMOTE SENSING IMAGE CLASSIFICATION BASED ON QUANTUM ENTANGLEMENT." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W10 (February 7, 2020): 667–70. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w10-667-2020.

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Abstract. Aiming at the problems of low accuracy and slow speed in the current remote sensing image classification algorithm,In order to improve remote sensing image classification, a quantum entanglement algorithm is proposed.The model transforms the classification process of remote sensing image into a random self-organization process of quantum particles in the state configuration space. The state configuration formed by entanglement of quantum particles evolves with time and finally converges to an average probability distribution.Taking Kunming city of Yunnan province as the research area, this paper compares the classification method in this paper with the traditional remote sensing classification method by using the 02C image data of yuanyuan1.Compared with other classification methods, the classification accuracy of this paper meets the requirements.

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Meyer, Johannes Jakob. "Fisher Information in Noisy Intermediate-Scale Quantum Applications." Quantum 5 (September 9, 2021): 539. http://dx.doi.org/10.22331/q-2021-09-09-539.

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The recent advent of noisy intermediate-scale quantum devices, especially near-term quantum computers, has sparked extensive research efforts concerned with their possible applications. At the forefront of the considered approaches are variational methods that use parametrized quantum circuits. The classical and quantum Fisher information are firmly rooted in the field of quantum sensing and have proven to be versatile tools to study such parametrized quantum systems. Their utility in the study of other applications of noisy intermediate-scale quantum devices, however, has only been discovered recently. Hoping to stimulate more such applications, this article aims to further popularize classical and quantum Fisher information as useful tools for near-term applications beyond quantum sensing. We start with a tutorial that builds an intuitive understanding of classical and quantum Fisher information and outlines how both quantities can be calculated on near-term devices. We also elucidate their relationship and how they are influenced by noise processes. Next, we give an overview of the core results of the quantum sensing literature and proceed to a comprehensive review of recent applications in variational quantum algorithms and quantum machine learning.

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