分类： 光学 >> 量子光学 提交时间： 2023-02-19
摘要： Spintronics and quantum information science are two promising candidates for innovating information processing technologies. The combination of these two fields enables us to build solid-state platforms for studying quantum phenomena and for realizing multi-functional quantum tasks. For a long time, however, the intersection of these two fields was limited. This situation has changed significantly over the last few years because of the remarkable progress in coding and processing information using magnons. On the other hand, significant advances in understanding the entanglement of quasi-particles and in designing high-quality qubits and photonic cavities for quantum information processing provide physical platforms to integrate magnons with quantum systems. From these endeavours, the highly interdisciplinary field of quantum magnonics emerges, which combines spintronics, quantum optics and quantum information science.Here, we give an overview of the recent developments concerning the quantum states of magnons and their hybridization with mature quantum platforms. First, we review the basic concepts of magnons and quantum entanglement and discuss the generation and manipulation of quantum states of magnons, such as single-magnon states, squeezed states and quantum many-body states including Bose-Einstein condensation and the resulting spin superfluidity. We discuss how magnonic systems can be integrated and entangled with quantum platforms including cavity photons, superconducting qubits, nitrogen-vacancy centers, and phonons for coherent information transfer and collaborative information processing. The implications of these hybrid quantum systems for non-Hermitian physics and parity-time symmetry are highlighted, together with applications in quantum memories and high-precision measurements. Finally, we present an outlook on the opportunities in quantum magnonics.
分类： 天文学 >> 天文学 提交时间： 2023-02-19
摘要： Primordial B-mode detection is one of the main goals of the current and future CMB experiments. However, the weak B-mode signal is overshadowed by several Galactic polarized emissions, such as the thermal dust emission and the synchrotron radiation. Subtracting the foreground components from CMB observations is one of the key challenges in the search for primordial B-mode signal. Here, we construct a deep convolutional neural network (CNN) model, called CMBFSCNN (Cosmic Microwave Background Foreground Subtraction with CNN), which can cleanly remove various foreground components from the simulated CMB observational maps with the sensitivity of the CMB-S4 experiment. The noisy CMB Q (or U) maps are recovered with a mean absolute difference of $0.018 \pm 0.023\ \mu$K (or $0.021 \pm 0.028\ \mu$K). To remove residual instrumental noise in the foreground-cleaned map, inspired by the Needlet Internal Linear Combination method, we divide the whole data into two ``half-split maps'' which share the same sky signal but with uncorrelated noise, and perform the cross-correlation technique to reduce the instrumental noise effect at the power spectrum level. We find that the CMB EE and BB power spectra can be precisely recovered with significantly reduced noise effects. Finally, we apply this pipeline on the current Planck observations. As expected, various foregrounds have been cleanly removed on the Planck observational maps and the recovered EE and BB power spectra are in good agreement with the Planck official results.
分类： 天文学 >> 天文学 提交时间： 2023-02-19
摘要： Primordial B-mode detection is one of the main goals of the current and future CMB experiments. However, the weak B-mode signal is overshadowed by several Galactic polarized emissions, such as the thermal dust emission and the synchrotron radiation. Subtracting the foreground components from CMB observations is one of the key challenges in the search for primordial B-mode signal. Here, we construct a deep convolutional neural network (CNN) model, called CMBFSCNN (Cosmic Microwave Background Foreground Subtraction with CNN), which can cleanly remove various foreground components from the simulated CMB observational maps with the sensitivity of the CMB-S4 experiment. The noisy CMB Q (or U) maps are recovered with a mean absolute difference of $0.018 \pm 0.023\ \mu$K (or $0.021 \pm 0.028\ \mu$K). To remove residual instrumental noise in the foreground-cleaned map, inspired by the Needlet Internal Linear Combination method, we divide the whole data into two ``half-split maps'' which share the same sky signal but with uncorrelated noise, and perform the cross-correlation technique to reduce the instrumental noise effect at the power spectrum level. We find that the CMB EE and BB power spectra can be precisely recovered with significantly reduced noise effects. Finally, we apply this pipeline on the current Planck observations. As expected, various foregrounds have been cleanly removed on the Planck observational maps and the recovered EE and BB power spectra are in good agreement with the Planck official results.
分类： 天文学 >> 天文学 提交时间： 2023-02-19
摘要： The cosmic microwave background (CMB), carrying the inhomogeneous information of the very early universe, is of great significance for understanding the origin and evolution of our universe. However, observational CMB maps contain serious foreground contaminations from several sources, such as galactic synchrotron and thermal dust emissions. Here, we build a deep convolutional neural network (CNN) to recover the tiny CMB signal from various huge foreground contaminations. Focusing on the CMB temperature fluctuations, we find that the CNN model can successfully recover the CMB temperature maps with high accuracy, and that the deviation of the recovered power spectrum $C_\ell$ is smaller than the cosmic variance at $\ell>10$. We then apply this method to the current Planck observation, and find that the recovered CMB is quite consistent with that disclosed by the Planck collaboration, which indicates that the CNN method can provide a promising approach to the component separation of CMB observations. Furthermore, we test the CNN method with simulated CMB polarization maps based on the CMB-S4 experiment. The result shows that both the EE and BB power spectra can be recovered with high accuracy. Therefore, this method will be helpful for the detection of primordial gravitational waves in current and future CMB experiments. The CNN is designed to analyze two-dimensional images, thus this method is not only able to process full-sky maps, but also partial-sky maps. Therefore, it can also be used for other similar experiments, such as radio surveys like the Square Kilometer Array.
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