Frontiers of Physics最新文献

We present a theoretical investigation of Majorana zero mode (MZM) assisted spin pumping which consists of a quantum dot (QD) and two normal leads. When the coupling between the MZM and the QD is absent, d.c. pure spin current can be excited by a rotating magnetic field where low energy spin down electrons are flipped to high energy spin up electrons by absorbing photons. However, when the coupling is turned on, the d.c. pure spin current vanishes, and an a.c. charge current emerges with its magnitude independent of the coupling strength. We reveal that this change is due to the formation of a highly localized MZM assisted topological Andreev state at the Fermi level, which allows only the injection of electron pairs with opposite spin into the QD. By absorbing or emitting photons, the electron pairs are separated to opposite spin electrons, and then return back to the lead again, generating an a.c. charge current without spin polarization. We demonstrate the switching from d.c. pure spin current to a.c. charge current based on both Kitaev model and a more realistic topological superconductor nanowire. Although this switching can also be induced by partially separated Andreev bound state (ps-ABS) in the topological trivial phase, it is extremely unstable and highly sensitive to the Zeeman field, which is different from the switching induced by MZM. Our result suggests that quantum spin pumping may be a feasible local transport method for detecting the presence of MZMs at the ends of a superconducting nanowire.

Some aspects of atom-field interactions in curved spacetime are reviewed. Of great interest are quantum radiative and entanglement processes arising out of Rindler and black hole spacetimes, which involve the role of Hawking–Unruh and dynamical Casimir effects. Most of the discussion surrounds the radiative part of interactions. For this, we specifically reassess the conventional understandings of atomic radiative transitions and energy level shifts in curved spacetime. We also briefly outline the status quo of entanglement dynamics study in curved spacetime, and highlight literature related to some novel insights, like entanglement harvesting. On one hand, the study of the role played by spacetime curvature in quantum radiative and informational phenomena has implications for fundamental physics, notably the gravity-quantum interface. In particular, one examines the viability of the Equivalence Principle, which is at the heart of Einstein’s general theory of relativity. On the other hand, it can be instructive for manipulating quantum information and light propagation in arbitrary geometries. Some issues related to nonthermal effects of acceleration are also discussed.

The spectral form factor (SFF) can probe the eigenvalue statistic at different energy scales as its time variable varies. In closed quantum chaotic systems, the SFF exhibits a universal dip-ramp-plateau behavior, which reflects the spectrum rigidity of the Hamiltonian. In this work, we explore the general properties of SFF in open quantum systems. We find that in open systems the SFF first decays exponentially, followed by a linear increase at some intermediate time scale, and finally decreases to a saturated plateau value. We derive general relations between (i) the early-time decay exponent and Lindblad operators; (ii) the long-time plateau value and the number of steady states. We also explain the effective field theory perspective of general behaviors. We verify our theoretical predictions by numerically simulating the Sachdev–Ye–Kitaev (SYK) model, random matrix theory (RMT), and the Bose–Hubbard model.

In order to fulfill the urgent requirements of functional products, circuit integration of different functional devices are commonly utilized. Thus, issues including production cycle, cost, and circuit crosstalk will get serious. Neuromorphic computing aims to break through the bottle neck of von Neumann architectures. Electronic devices with multi-operation modes, especially neuromorphic devices with multi-mode cognitive activities, would provide interesting solutions. Here, pectin/chitosan hybrid electrolyte gated oxide neuromorphic transistor was fabricated. With extremely strong proton related interfacial electric-double-layer coupling, the device can operate at low voltage of below 1 V. The device can also operate at multi-operation mode, including bottom gate mode, coplanar gate and pseudo-diode mode. Interestingly, the artificial synapse can work at low voltage of only 1 mV, exhibiting extremely low energy consumption of ∼7.8 fJ, good signal-to-noise ratio of ∼229.6 and sensitivity of ∼23.6 dB. Both inhibitory and excitatory synaptic responses were mimicked on the pseudo-diode, demonstrating spike rate dependent plasticity activities. Remarkably, a linear classifier is proposed on the oxide neuromorphic transistor under synaptic metaplasticity mechanism. These results suggest great potentials of the oxide neuromorphic devices with multi-mode cognitive activities in neuromorphic platform.

Orbital angular momentums (OAMs) greatly enhance the channel capacity in free-space optical communication. However, demodulation of superposed OAM to recognize them separately is always difficult, especially upon multiplexing more OAMs. In this work, we report a directly recognition of multiplexed fractional OAM modes, without separating them, at a resolution of 0.1 with high accuracy, using a multi-task deep learning (MTDL) model, which has not been reported before. Namely, two-mode, four-mode, and eight-mode superposed OAM beams, experimentally generated with a hologram carrying both phase and amplitude information, are well recognized by the suitable MTDL model. Two applications in information transmission are presented: the first is for 256-ary OAM shift keying via multiplexed fractional OAMs; the second is for OAM division multiplexed information transmission in an eightfold speed. The encouraging results will expand the capacity in future free-space optical communication.

We show that the manifold of quantum states is endowed with a rich and nontrivial geometric structure. We derive the Fubini–Study metric of the projective Hilbert space of a multi-qubit quantum system, endowing it with a Riemannian metric structure, and investigate its deep link with the entanglement of the states of this space. As a measure, we adopt the entanglement distance E preliminary proposed in Phys. Rev. A 101, 042129 (2020). Our analysis shows that entanglement has a geometric interpretation: E(∣ψ〉) is the minimum value of the sum of the squared distances between ∣ψ〉 and its conjugate states, namely the states ν^μ · σ^μ∣ψ〉, where ν^μ are unit vectors and μ runs on the number of parties. Within the proposed geometric approach, we derive a general method to determine when two states are not the same state up to the action of local unitary operators. Furthermore, we prove that the entanglement distance, along with its convex roof expansion to mixed states, fulfils the three conditions required for an entanglement measure, that is: i) E(∣ψ〉) = 0 iff ∣ψ〉 is fully separable; ii) E is invariant under local unitary transformations; iii) E does not increase under local operation and classical communications. Two different proofs are provided for this latter property. We also show that in the case of two qubits pure states, the entanglement distance for a state ∣ψ〉 coincides with two times the square of the concurrence of this state. We propose a generalization of the entanglement distance to continuous variable systems. Finally, we apply the proposed geometric approach to the study of the entanglement magnitude and the equivalence classes properties, of three families of states linked to the Greenberger–Horne–Zeilinger states, the Briegel Raussendorf states and the W states. As an example of application for the case of a system with continuous variables, we have considered a system of two coupled Glauber coherent states.

We study mathematical, physical and computational aspects of the stabilizer formalism arising in quantum information and quantum computation. The measurement process of Pauli observables with its algorithm is given. It is shown that to detect genuine entanglement we need a full set of stabilizer generators and the stabilizer witness is coarser than the GHZ (Greenberger–Horne–Zeilinger) witness. We discuss stabilizer codes and construct a stabilizer code from a given linear code. We also discuss quantum error correction, error recovery criteria and syndrome extraction. The symplectic structure of the stabilizer formalism is established and it is shown that any stabilizer code is unitarily equivalent to a trivial code. The structure of graph codes as stabilizer codes is identified by obtaining the respective stabilizer generators. The distance of embeddable stabilizer codes in lattices is obtained. We discuss the Knill–Gottesman theorem, tableau representation and frame representation. The runtime of simulating stabilizer gates is obtained by applying stabilizer matrices. Furthermore, an algorithm for updating global phases is given. Resolution of quantum channels into stabilizer channels is shown. We discuss capacity achieving codes to obtain the capacity of the quantum erasure channel. Finally, we discuss the shadow tomography problem and an algorithm for constructing classical shadow is given.

MXenes, a novel class of 2D transition metal carbides and nitrides, have recently emerged as a promising candidate in the quest for efficient catalysts for the hydrogen evolution reaction. To enhance the performance of 2D MXenes with modest or poor catalytic efficiency, a particularly prosperous strategy involves doping with transition and noble metal atoms. Taking the Nb₄C₃O₂ monolayer as a model, we explore substitutional metallic impurities, which serve as single-atom catalysts embedded within the Nb₄C₃O₂ surface. Our findings demonstrate the ability to finely tune the atomic H binding energy within a 0.6 eV range, showing the potential for precise control in catalytic applications. Across different transition and noble metals, the single atoms integrated into Nb₄C₃O₂ effectively adjust the free energy of H adsorption at nearby O atoms, achieving values comparable to or superior to Pt catalysts. A comprehensive examination of the electronic properties around the impurities reveals a correlation between changes in local reactivity and charge transfer to neighboring O atoms, where H atoms bind.

Since the isolation of graphene, two-dimensional (2D) materials have attracted increasing interest because of their excellent chemical and physical properties, as well as promising applications. Nonetheless, particular challenges persist in their further development, particularly in the effective identification of diverse 2D materials, the domains of large-scale and high-precision characterization, also intelligent function prediction and design. These issues are mainly solved by computational techniques, such as density function theory and molecular dynamic simulation, which require powerful computational resources and high time consumption. The booming deep learning methods in recent years offer innovative insights and tools to address these challenges. This review comprehensively outlines the current progress of deep learning within the realm of 2D materials. Firstly, we will briefly introduce the basic concepts of deep learning and commonly used architectures, including convolutional neural and generative adversarial networks, as well as U-net models. Then, the characterization of 2D materials by deep learning methods will be discussed, including defects and materials identification, as well as automatic thickness characterization. Thirdly, the research progress for predicting the unique properties of 2D materials, involving electronic, mechanical, and thermodynamic features, will be evaluated succinctly. Lately, the current works on the inverse design of functional 2D materials will be presented. At last, we will look forward to the application prospects and opportunities of deep learning in other aspects of 2D materials. This review may offer some guidance to boost the understanding and employing novel 2D materials.

Principal component analysis (PCA) is a widely used tool in machine learning algorithms, but it can be computationally expensive. In 2014, Lloyd, Mohseni & Rebentrost proposed a quantum PCA (qPCA) algorithm [Nat. Phys. 10, 631 (2014)] that has not yet been experimentally demonstrated due to challenges in preparing multiple quantum state copies and implementing quantum phase estimations. In this study, we presented a hardware-efficient approach for qPCA, utilizing an iterative approach that effectively resets the relevant qubits in a nuclear magnetic resonance (NMR) quantum processor. Additionally, we introduced a quantum scattering circuit that efficiently determines the eigenvalues and eigenvectors (principal components). As an important application of PCA, we focused on classifying thoracic CT images from COVID-19 patients and achieved high accuracy in image classification using the qPCA circuit implemented on the NMR system. Our experiment highlights the potential of near-term quantum devices to accelerate qPCA, opening up new avenues for practical applications of quantum machine learning algorithms.