Temporal interference with frequency-controllable long photons from independent cold atomic sources

Qian, Peng; Gu, Zhenjie; Wen, Rong; Zhang, Weiping; Chen, Jiefei

doi:10.1103/physreva.97.013806

Cited by 6 publications

(2 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Regarding the few‐layered WSe 2 nanosheet, the out‐of‐plane A 1g mode (≈259 cm −1 ) and in‐plane

E_{2g}^1

(≈249 cm −1 ) mode are in excellent agreement with the previously reported findings. [ 37,38 ] Figure 2f illustrates the photoluminescence ( PL ) spectroscopy of WSe 2 and GaTe 0.5 Se 0.5 under 405 nm laser excitation. Notably, exciton resonance peaks can be observed at 690 (GaTe 0.5 Se 0.5 : blue line in Figure 2f) and 752 nm (WSe 2 : purple line in Figure 2f), consistent well with the optical bandgap values E g = 1240/λ obtained from the theoretical calculation.…”

Section: Resultsmentioning

confidence: 99%

Spectrometer‐Less Remote Sensing Image Classification Based on Gate‐Tunable van der Waals Heterostructures

Yu,

Zhong,

Xiong

et al. 2024

Advanced Science

View full text Add to dashboard Cite

Remote sensing technology, which conventionally employs spectrometers to capture hyperspectral images, allowing for the classification and unmixing based on the reflectance spectrum, has been extensively applied in diverse fields, including environmental monitoring, land resource management, and agriculture. However, miniaturization of remote sensing systems remains a challenge due to the complicated and dispersive optical components of spectrometers. Here, m‐phase GaTe0.5Se0.5 with wide‐spectral photoresponses (250–1064 nm) and stack it with WSe2 are utilizes to construct a two‐dimensional van der Waals heterojunction (2D‐vdWH), enabling the design of a gate‐tunable wide‐spectral photodetector. By utilizing the multi‐photoresponses under varying gate voltages, high accuracy recognition can be achieved aided by deep learning algorithms without the original hyperspectral reflectance data. The proof‐of‐concept device, featuring dozens of tunable gate voltages, achieves an average classification accuracy of 87.00% on 6 prevalent hyperspectral datasets, which is competitive with the accuracy of 250–1000 nm hyperspectral data (88.72%) and far superior to the accuracy of non‐tunable photoresponse (71.17%). Artificially designed gate‐tunable wide‐spectral 2D‐vdWHs GaTe0.5Se0.5/WSe2‐based photodetector present a promising pathway for the development of miniaturized and cost‐effective remote sensing classification technology.

show abstract

“…Regarding the few‐layered WSe 2 nanosheet, the out‐of‐plane A 1g mode (≈259 cm −1 ) and in‐plane

E_{2g}^1

Section: Resultsmentioning

confidence: 99%

Spectrometer‐Less Remote Sensing Image Classification Based on Gate‐Tunable van der Waals Heterostructures

Yu,

Zhong,

Xiong

et al. 2024

Advanced Science

View full text Add to dashboard Cite

show abstract

“…This improvement will raise the sensitivity of many NV-center applications such as magnetometry, thermometry and electric field sensor. The ML method is easily extendable to other systems where the discrimination of states is basically relying on the readout of time-resolvable fluorescence via singlephoton detection schemes 31,32 , such as the spin qubit readout in single quantum dot 33 , and silicon-vacancy center in diamond 34 . Moreover, in the fields involving fluorescence spectroscopy in biomedical engineering, our ML learning method might be employed to help distinguishing different enzymatic reaction steps 35 , and malignant tumors from non-malignant tissues in cancer detection 36 .…”

mentioning

confidence: 99%

Machine-learning-assisted electron-spin readout of nitrogen-vacancy center in diamond

Qian,

Lin,

Zhou

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Machine learning is a powerful tool in finding hidden data patterns for quantum information processing. Here, we introduce this method into the optical readout of electron-spin states in diamond via single-photon collection and demonstrate improved readout precision at room temperature. The traditional method of summing photon counts in a time gate loses all the timing information crudely. We find that changing the gate width can only optimize the contrast or the state variance, not both. In comparison, machine learning adaptively learns from time-resolved fluorescence data, and offers the optimal data processing model that elaborately weights each time bin to maximize the extracted information. It is shown that our method can repair the processing result from imperfect data, reducing 7% in spin readout error while optimizing the contrast. Note that these improvements only involve recording photon time traces and consume no additional experimental time, they are thus robust and free. Our machine learning method implies a wide range of applications in precision measurement and optical detection of states.

show abstract

Machine-learning-assisted electron-spin readout of nitrogen-vacancy center in diamond

Qian

Lin

Zhou

et al. 2021

Applied Physics Letters

View full text Add to dashboard Cite

Machine learning is a powerful tool in finding hidden data patterns for quantum information processing. Here, we introduce this method into the optical readout of electron-spin states in diamond via single-photon collection and demonstrate improved readout precision at room temperature. The traditional method of summing photon counts in a time gate loses all the timing information crudely. We find that changing the gate width can only optimize the contrast or the state variance, not both. In comparison, machine learning adaptively learns from time-resolved fluorescence data and offers the optimal data processing model that elaborately weights each time bin to maximize the extracted information. It is shown that our method can repair the processing result from imperfect data, reducing 7% in spin readout error while optimizing the contrast. Note that these improvements only involve recording photon time traces and consume no additional experimental time, and they are, thus, robust and free. Our machine learning method implies a wide range of applications in the precision measurement and optical detection of states.

show abstract

Temporal interference with frequency-controllable long photons from independent cold atomic sources

Cited by 6 publications

References 43 publications

Spectrometer‐Less Remote Sensing Image Classification Based on Gate‐Tunable van der Waals Heterostructures

Spectrometer‐Less Remote Sensing Image Classification Based on Gate‐Tunable van der Waals Heterostructures

Machine-learning-assisted electron-spin readout of nitrogen-vacancy center in diamond

Machine-learning-assisted electron-spin readout of nitrogen-vacancy center in diamond

Contact Info

Product

Resources

About