Quantum Frontiers最新文献

The storage of quantum states and information is essential for enabling large quantum networks. The direct implementation of storage in magnonic systems, which are emerging as crucial components in quantum networks, has also garnered attention. In this study, we present experimental investigations of magnomechanical microwave storage for the first time. By reducing the ambient temperature to 8 K, we can achieve a mechanical mode with a narrow linewidth as low as 6.4 Hz, resulting in an energy decay time of 24.8 ms. Furthermore, we employ Ramsey interferometry to investigate the coherence of the magnomechanical memory. The mechanical interference can be utilized to evaluate the decoherence lifetime of 19.5 ms. Our proposed scheme provides the potential to utilize magnomechanical systems as quantum memory for photonic quantum information.

Quantum key distribution (QKD) holds the potential to establish secure keys over long distances. The distance of point-to-point QKD secure key distribution is primarily impeded by the transmission loss inherent to the channel. In the quest to realize a large-scale quantum network, increasing the QKD distance under current technology is of great research interest. Here we adopt the 3-intensity sending-or-not-sending twin-field QKD (TF-QKD) protocol with the actively-odd-parity-pairing method. The experiment demonstrates the feasibility of secure QKD over a 1002 km fibre channel considering the finite size effect. The secure key rate is (3.11times 10^{-12}) per pulse at this distance. Furthermore, by optimizing parameters for shorter fiber distances, we conducted performance tests on key distribution for fiber lengths ranging from 202 km to 505 km. Notably, the secure key rate for the 202 km, the normal distance between major cities, reached 111.74 kbps.

We study the robustness of machine learning approaches to adversarial perturbations, with a focus on supervised learning scenarios. We find that typical phase classifiers based on deep neural networks are extremely vulnerable to adversarial perturbations: adding a tiny amount of carefully crafted noises into the original legitimate examples will cause the classifiers to make incorrect predictions at a notably high confidence level. Through the lens of activation maps, we find that some important underlying physical principles and symmetries remain to be adequately captured for classifiers with even near-perfect performance. This explains why adversarial perturbations exist for fooling these classifiers. In addition, we find that, after adversarial training the classifiers will become more consistent with physical laws and consequently more robust to certain kinds of adversarial perturbations. Our results provide valuable guidance for both theoretical and experimental future studies on applying machine learning techniques to condensed matter physics.

We study the emergence of non-Fermi liquid on heterostructure interfaces where there exists an infinite number of critical boson modes accounting for the magnetic fluctuations in two spatial dimensions. The interfacial Dzyaloshinskii-Moriya interaction naturally arises in magnetic interactions due to the absence of inversion symmetry, resulting in a degenerate contour for the low-energy bosonic modes in the momentum space which simultaneously becomes critical near the magnetic phase transition. The itinerant electrons are scattered by the critical boson contour via the Yukawa coupling. When the boson contour is much smaller than the Fermi surface, it is shown that, there exists a regime with a dynamic critical exponent ({z=3}) while the boson contour still controls the low-energy magnetic fluctuations. Using a self-consistent renormalization calculation for this regime, we uncover a prominent non-Fermi liquid behavior in the resistivity with a characteristic temperature scaling power. These findings open up new avenues for understanding boson-fermion interactions and the novel fermionic quantum criticality.