ABSTRACT The electrical control of perpendicular magnetization in ferromagnetic (FM)/antiferromagnetic (AFM) heterostructures is crucial for next‐generation spintronic devices. In this micromagnetic study, we demonstrate perpendicular exchange bias in the CoFeB/IrMn system and achieve field‐free perpendicular magnetization switching (PEB) of CoFeB via spin–orbit torque generated by the IrMn layer. Magnetization switching occurs through domain wall nucleation at the edges, but high anisotropy pinned spins at the interface generate a pinning force on the CoFeB layer that hinders domain wall propagation. Lower current densities reduce domain wall velocity, resulting in longer switching times. The application of an in‐plane magnetic field during SOT‐induced switching gives rise to oscillatory dynamics; however, increasing the damping effectively suppresses these precessional effects. Lower damping values, such as 0.025, lead to incomplete switching due to insufficient SOT energy dissipation, while increasing the damping value to 0.05 and above enables deterministic switching. The total energy increases with pinning density, likely due to the additional pinning energy introduced by interfacial pinned spins. Overall, this SOT‐driven, field‐free perpendicular switching presents a promising route toward advanced spintronic and quantum technology applications.

ABSTRACT We propose a scheme for the simultaneous generation of the strong squeezing breaking the 3dB limit for both mechanical and low‐frequency LC resonators in an electro–optomechanical system, in which the optical cavity is subjected to two‐tone driving. By applying red‐ and blue‐detuned driving fields simultaneously, the Bogoliubov modes of two coupled low‐frequency resonators can be cooled to their quantum ground state. This ground‐state cooling enables the generation of strong quantum squeezing. Moreover, the proposed scheme is highly robust against elevated environmental temperatures. This holds significant importance for quantum information processing and quantum precision measurement, especially the study of quantum properties at a more macroscopic level.

ABSTRACT High pressure, as a fundamental thermodynamic parameter, can be used as a clean and efficient tool to regulate interatomic distances in quantum materials, enabling precise control of lattice structure, electron correlation, spin–orbit coupling, and band topology without introducing additional chemical impurities. In this review, we summarize that high‐pressure changes in lattice symmetry, orbital hybridization, band structure, magnetic order, and electron–phonon coupling induce the emergence of new quantum states. We also distinguish that high pressure not only compresses lattice structures, but also enhances quantum effects that lead to exotic states, including unconventional superconductivity with elevated transition temperatures, disproportionate charge‐density waves, electron‐correlated driven Mott insulators, topologically protected phases versus stable topologically protected states, and fractional excited quantum spin liquids. In addition, we reconcile the contradictory experimental results in the current research field, such as the nonmonotonic variation of superconducting transition temperature with pressure in kagome metals and the controversial topological phase transition conditions in TaAs. Finally, we outline the potential applications of high‐pressure technologies, including high‐temperature superconductors, quantum computing, and emerging materials.

ABSTRACT We propose a theoretical protocol for generating long‐distance Greenberger‐Horne‐Zeilinger states in a driven hybrid quantum system. The system consists of a central magnon coupled to multiple nitrogen–vacancy center spins positioned at a distance from the surface of the yttrium‐iron‐garnet sphere. A significant enhancement in the effective magnon‐spin coupling strength is achieved by driving the magnon with Kerr term. We analyze the dependence of the squeezing parameter on the driving field and adiabatically eliminate the magnonic mode under large detuning conditions via the Schrieffer–Wolff transformation, thereby establishing an effective all‐to‐all one‐axis‐twisting type spin–spin interaction essential for Greenberger–Horne–Zeilinger generation. Numerical simulations show that multi‐qubit Greenberger–Horne–Zeilinger states can be achieved even in the presence of dissipation. Furthermore, we investigate the impact of both inhomogeneous frequency broadening and non‐uniform coupling strengths between the magnon and spins. A cavity protection mechanism is introduced to mitigate frequency inhomogeneous broadening, while the tolerance to coupling inhomogeneity is also quantitatively evaluated.

ABSTRACT We review existing classical simulation methods for performing fermionic Gaussian operations and develop new methods to address the gap by adhering to the fundamental theoretical framework established by Bravyi [Quantum Info. Comput. 5, 216 (2005)] for the most general fermionic Gaussian processes. Throughout this attempt, the focus remains on the unified approach that can be applied to generic fermionic Gaussian operations. This is beneficial since the selection of simulation methods has often been based on an ad hoc choice, heavily influenced by the specific model and circumstances, rather than on a systematic approach.

ABSTRACT The electron spin of nitrogen‐vacancy center and the phonon mode of mechanical resonator are widely adopted to explore information science and technology, in which the preparation of entangled states in a fast and robust manner is of significance. Here we present a scheme for creating an entangled W state by the technique of shortcut to adiabaticity. Two electron spins are magnetically coupled to a common mechanical resonator, and each spin is individually driven by a classical pulse. By encoding the dressed states of the microwave‐driven spin to a logic qubit manifold, we obtain an effective spin‐resonator‐spin system. By suitably engineering the invariant‐based shortcut with constant Rabi rates, one‐step generation of a tripartite entangled state can be realized without the adiabatic constraint. We further analyze the decoherence effects and the robustness against the Rabi rate inaccuracies and timing error. The strategy could provide a promising avenue to generate entangled states with the hybrid system.

ABSTRACT In the last decade, the rapid evolution of ultrafast laser spectroscopy has provided new impetus to the branch of chemistry dealing with tunneling processes involving electrons and light atoms. The possibility of measuring the time required by a particle to cross a barrier opens new perspectives for understanding the kinetics and dynamics of tunneling processes. In this study, a model is developed to estimate the tunneling time in tautomerization reactions based on proton transfer through a classically forbidden barrier. The proposed approach uses the asymptotic values of the wavefunction to compute the dwell time, which is the average time spent by the proton inside the potential barrier. The theory is formulated for both static barriers, which are good approximations for isolated molecules, and dynamic barriers to account for the effects of the chemical bath. The model performance is evaluated by two numerical simulations, in which a proton is transferred via tunneling in a double‐well potential; the tunneling barrier is approximated by a static and dynamical rectangular potential. A possible extension of the theory to open systems is briefly discussed within the framework of the Caldeira–Leggett model.

ABSTRACT Quantum neural networks (QNNs) are advancing rapidly, but their security issues must not be overlooked. Backdoor attacks, as a stealthy and highly threatening attack method, have caused significant impacts on classical neural networks. Moreover, backdoor attacks have recently been proven to pose a significant threat to quantum neural networks as well. Currently, all existing methods in quantum machine learning backdoor research are designed for classical data samples. These methods all involve poisoning classical data samples first and then quantizing them, with no existing approaches that directly target quantum state data for poisoning. This paper presents a novel backdoor attack method against quantum neural networks by utilizing the quantum state—universal adversarial perturbations (QS‐UAP). This method, for the first time, constructs a backdoor trigger specifically for quantum‐state data based on quantum generative models. Moreover, this method enhances the quality of the backdoor trigger by integrating knowledge distillation with frequency‐domain constraints. The proposed method embeds a backdoor into the target model by poisoning quantum data samples. Experimental results demonstrate that with a 10% poisoning rate, the attack success rates exceed 97% across three structurally distinct QNN architectures.

ABSTRACT In the node classification task on graphs, mainstream graph neural networks often adopt the same neighborhood aggregation strategy for all nodes. This approach results in biased learning toward features from the majority nodes while devaluing those from the minority nodes under class imbalance. In particular, for marginal nodes, limited neighboring information can severely impact classification performance. To address this challenge, this paper proposes a feature fusion‐based hybrid quantum‐classical graph residual neural network (QGRNN). Leveraging the nonlinear expressive capacity of qubits in modeling complex feature interactions, the model innovatively integrates a structure‐driven node selection mechanism with a quantum feature enhancement module, while also dynamically fusing classical features and Hamiltonian expectation values through a gated residual fusion mechanism to compensate for representational deficiencies of marginal nodes overlooked by classical methods. Experimental results show that QGRNN consistently outperforms baselines across a range of node classification tasks. In binary and ternary classification settings, it exhibits strong discriminative capability and robustness, especially maintaining high accuracy under severe class imbalance. In addition, QGRNN also demonstrates strong generality in other tasks, in recommendation scenarios, achieving an average improvement of 36.3% on NDCG@5, 44.3% on Recall@5, and 21.9% on Recall@10 compared to the baseline.