By utilizing Eq.(3) into the Eq.(1) with \(h_{1}=0,\) we achieve
$$\begin{aligned} \left\{ \begin{array}{c} \omega M^{^{\prime \prime }}-MN^{^{\prime }}=0, \\ \omega N^{^{\prime \prime }}+MM^{^{\prime }}=0. \end{array} \right. \end{aligned}$$
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Category: 4. Physics
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Pressure tuning of Kitaev spin liquid candidate Na3Co2SbO6
Kitaev, A. Anyons in an exactly solved model and beyond. Ann. Phys. 321, 2–111 (2006).
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Kitaev, A. Y. Fault-tolerant quantum computation by anyons. Ann. Phys. 303, 2–30…
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Mitigation of sulfide adsorption in natural gas by silanized stainless steel: insights from density functional theory
Sulfur compounds like hydrogen sulfide, methyl mercaptan, ethyl mercaptan, and thionyl carbon pose safety risks1,2, induce pipeline corrosion, and impacting on natural gas quality. Consequently, monitoring these compounds throughout the stages of…
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Classical simulability of constant-depth linear-optical circuits with noise
Linear-optical circuits with single photons
Consider M-mode linear-optical circuits with N single-photon inputs and arbitrary local measurements. Linear-optical circuits consist of beam-splitter layers, which may be geometrically non-local, i.e.,…
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Microscopic theory of polariton group velocity renormalization
Model system
We use the Generalized Holstein-Tavis-Cummings (GHTC) Hamiltonian23,24,25,26 to describe N excitons interacting with \({{{\mathcal{M}}}}\) cavity modes, and \(N\gg {{{\mathcal{M}}}}\) in line with typical experimental conditions….
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Cavity-enhanced continuous-wave microscopy with potentially unstable cavity length
Cavity parameters
The cavity length is defined by the 4f requirement to be \(\sim 30\,\text {cm}\). Accounting for the optical path length differences introduced by the lenses results in an actual length of approximately \(30.6\,\text {cm}\). This…
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A dual input dual spliced network with data augmentation for robust modulation recognition in communication countermeasures
PET-CGDNN model
The proposed Parameter Estimation and Transformation-based CNN-GRU Deep Neural Network (PET-CGDNN) model consists of a parameter estimator, a parameter transformer, and a hybrid neural network, as depicted in Fig. 1.
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Tunable reciprocal and nonreciprocal contributions to 1D Coulomb drag
Device operation and wires characterization
A schematic of the vertically-coupled quantum wires devices used in this work is shown in Fig. 1b, with a typical optical image provided (see Supplementary Fig. 1). As the two wires are defined in two…
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Supervised learning of the Jaynes–Cummings Hamiltonian
Wang, X., Zhao, Y. & Pourpanah, F. Recent advances in deep learning. Int. J. Mach. Learn. Cybernet. 11, 747–750 (2020).
Young, T., Hazarika, D., Poria, S. & Cambria, E. Recent trends in deep…
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Quantum-like nonlinear interferometry with frequency-engineered classical light
Super-resolution can be explained via the definition of maximally entangled state, that can be written as25:
$$\begin{aligned} |\psi \rangle = \frac{|N,0\rangle _{ab} + e^{iN\phi }|0,N\rangle _{ab}}{\sqrt{2}}\,. \end{aligned}$$
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