1,720,977 research outputs found
Cavities with nonspherical mirrors for enhanced interaction between a quantum emitter and cavity photons
No full textWe propose a procedure for the significant enhancement of the strong-coupling rate between photons in an optical cavity and a single quantum emitter, such as an atom, quantum dot, or trapped ion. We show that specially designed, nonspherical mirrors can lead to cavity eigenmodes that exhibit a large field enhancement at the center of the cavity while inducing significantly less beam divergence and therefore smaller round trip losses and higher cooperativity than can be achieved by operating a spherical-mirror cavity in the near-concentric regime. We verify our designs using mode matching theory and discuss their robustness relative to different kinds of manufacturing deviations
Dataset for an article "Evolutionary algorithm to design high-cooperativity optical cavities"
No full textDataset to support an article "Evolutionary algorithm to design high-cooperativity optical cavities" in New Journal of Physics (accepted/in press)</span
Designing fiber-tip optical resonators for strong field enhancement
No full textFor many applications in quantum technology or optical sensing strong coupling between light and micro- or nano-particles is highly desirable. Fabry-Perot optical resonators formed between mirror-coated tips of two optical fibers lead to field enhancement and can be exploited for strong coupling of light to a particle, but the enhancement factor is still limited by geometrical restrictions. In our work we investigate new designs of such fiber-tip resonators where the shape of the mirrors is optimized to create interference patterns inside the resonator that lead to high peak intensities at the position of the particle. We use a range of approaches, such as analytical theory, evolutionary algorithms, and machine learning, to find the best designs. Our results suggest that significant field enhancement is possible with mirror shapes that deviate only moderately from spherical shapes. These can be fabricated by laser ablation, focused ion beam milling, or micromachining of fiber ends and could give rise to more precise optical sensors and faster quantum information processors
Cavities with Non-Spherical Mirrors for Enhanced Quantum Emitter-Cavity Photon Interaction
No full textThis dataset contains the results of numerical simulations supporting the corresponding article in Physical Review A "Cavities with Non-Spherical Mirrors for Enhanced Quantum Emitter-Cavity Photon Interaction" by D.V. Karpov and P. Horak.
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Dataset in support of the journal article 'Convolutional neural networks for mode on-demand high finesse optical resonator design'
No full textDataset to support article "Convolutional Neural Networks for Mode On-Demand
High Finesse Optical Resonator Design", accepted for publication in Scientific Reports
This dataset contains the data of Figures 3 and 4, and the numerical model to recreate Figures 5-7of the associated publication
The following data files are plain text files in CSV format (comma-separated values), readable by any text editor, spreadsheet editor, or numerical software (we use Microsoft Excel). All data descriptions and units are given as headers in the csv files.
figure3.csv: data for Fig. 3, true and predicted amplitude and period (in um) of the validation dataset
figure4.csv: data for Fig.4: mean error of the training dataset and of the validation dataset versus epoch number
"one peak" and "two peaks" directories:
Data in these directories contain the fully trained neural network structures and weights. Figures 5, 7 where generated from "one peak", Figure 6 from "two peaks".
The model is stored in the standard Keras/Tensorflow format: The model architecture, and training configuration (including the optimizer, losses, and metrics) are stored in saved_model.pb. The weights are saved in the variables/ directory.
For example, the "one peak" model can be loaded in Python by:
from tensorflow import keras
model = keras.models.load_model("one peak")
The predicted pair of (Amplitude, Period) for a target field E (cooperativity C on a spatial grid in a 100x150 array, formatted to Keras/Tensorflow style) is then obtained as:
predictions = model.predict(E)
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Evolutionary algorithm to design high-cooperativity optical cavities
No full textUsing an evolutionary algorithm combined with a gradient descent method we
design optical cavities with significantly enhanced strong coupling rates
between cavity photons and a single quantum emitter. Our approach allows us to
find specially designed non-spherical mirrors which lead to high-finesse cavity
eigenmodes with large field enhancement at the center of the cavity. The method
is based on adding consecutive perturbations to an initial spherical mirror
shape using the gradient descent method for optimization. We present mirror
profiles suitable for fabrication which demonstrate higher cavity cooperativity
than any spherical cavity of the same size. Finally, we demonstrate numerically
how such a cavity enhances the operation frequency and purity of coupling a
Ca ion to an optical fiber photon
Convolutional neural networks for mode on-demand high finesse optical resonator design
No full textWe demonstrate the use of machine learning through convolutional neural networks to solve inverse design problems of optical resonator engineering. The neural network finds a harmonic modulation of a spherical mirror to generate a resonator mode with a given target topology (“mode on-demand”). The procedure allows us to optimize the shape of mirrors to achieve a significantly enhanced coupling strength and cooperativity between a resonator photon and a quantum emitter located at the center of the resonator. In a second example, a double-peak mode is designed which would enhance the interaction between two quantum emitters, e.g., for quantum information processing
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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