All conclusions and results in this paper were based on the original, correct descriptions. Regarding the impact of these corrections, we clarify that: (1) all numerical results and performance comparisons remain fully valid; (2) all experiments were conducted using correctly implemented algorithms; and (3) no amendments to figures, tables, or quantitative claims are required. These are minor errors that do not affect the conclusions of the original paper.
We apologize for these errors.
We investigate the localization manipulation of skin and in-gap states in a one-dimensional nonreciprocal microring coupled-resonator optical waveguide array, where topological features of non-Hermitian phases emerge through modulated next-nearest-neighbor hopping. The non-Hermitian skin effect demonstrates both bidirectional and unidirectional localization behaviors within specific parameter regimes of next-nearest-neighbor hopping, as evidenced by the wave function distribution of the system. This distinctive localization facilitates an effective separation between in-gap states containing zero and nonzero energy edge states from bulk states, establishing a practical approach for the experimental observation of non-Hermitian topological phases. Through disorder analysis, we reveal the counterintuitive phenomenon that the localization of nonzero energy edge states strengthens with increasing next-nearest-neighbor disorder intensity. Additionally, we characterize skin transitions via spectral winding number calculations under periodic boundary conditions and construct a comprehensive skin phase diagram. Our findings not only provide a robust framework for realizing stable and distinguishable topological phases but also pave the way for investigating other complex non-Hermitian topological phenomena.
W states are essential resources in quantum communication and computation but are highly vulnerable to decoherence. We propose a three-stage, non-recursive entanglement distillation protocol that extracts a maximally entangled W state from multiple copies of amplitude-damped W states, applicable to both the standard and generalized W states. Unlike conventional recursive schemes, the proposed protocol imposes no constraints on the initial fidelity of the input states and achieves unit-fidelity distillation with improved efficiency and success probability. Instead of iteratively enhancing entanglement fidelity, our approach constructs a specific non-trivial sacrificial-state structure within the first two stages of the protocol. By sacrificing this intermediate state in the final stage, the protocol enables the distillation of a maximally entangled W state from an amplitude-damped input, offering a new non-recursive route to high-fidelity multipartite entanglement purification. Comparative analysis demonstrates that the proposed method significantly outperforms the weak-measurement-based distillation protocol across all decay rates. The IBM Qiskit simulations further validate the analytical predictions, confirming both the correctness and the practical feasibility of the proposed protocol.
In this research, we explore a novel hybrid channel architecture for preserving entanglement, specifically tailored for quantum communication applications. Our design incorporates a coupled multifluxonium system, consisting of two fluxonium qubits interconnected through capacitive coupling, considering the intricate dynamics of quantum properties. The hybrid channel exhibits a unique composition, featuring both thermal and local components, which dynamically evolve over time. To evaluate its performance, we employ a comprehensive set of metrics, including quantum statistical speeds (quantum Fisher information and Hilbert–Schmidt speed), fidelity, average fidelity, relative entropy of entanglement, Bures distance, and Von Neumann entropy. These measures provide a thorough understanding of the channel's effectiveness in transmitting quantum states. Our findings reveal that this hybrid channel achieves maximum entanglement and mixedness of the states, showcasing its exceptional capability for successful quantum state transmission. This breakthrough holds immense potential for advancing quantum communication and computing technologies. This study contributes to the development of robust quantum information processing systems, paving the way for future advancements in the field. We also describe a practical implementation of quantum teleportation based on the current channel via quantum state tomography.
Recently, a quantum blind signature scheme based on the -state for quantum supply chain finance has been proposed, which utilizes blindness to protect the privacy of participating firms. However, our analysis found that this scheme cannot resist the intercept-measurement-resend attack and the entanglement-measurement attack, and the blind operation is ineffective against the insider attacker. Importantly, the blind signer can achieve existential forgery through a valid signature. Then, an improved scheme was proposed that reduces quantum bit consumption, eliminates the need for quantum state swap tests, and extends the message space for signatures to the more universal classical bit space. We have constructed a security model that formally defines the security properties and adversary capabilities of the quantum blind signature. Through this model, the blindness and unforgeability of the improved scheme are reduced to the principles of quantum mechanics in four games. The vulnerabilities of the original scheme and the feasibility of the improved scheme in quantum computing have been verified through the Qiskit simulator.
Quantum computing has become increasingly practical in solving real-world problems due to advances in hardware and algorithms. In this paper, we aim to design, apply, and evaluate quantum machine learning and hybrid quantum-classical models in a few practical materials science tasks, i.e., predicting stacking fault energies and solutes that can ductilize magnesium. To this end, we adopt two different representative quantum algorithms, i.e., quantum support vector machines (QSVM) and quantum neural networks (QNN), and adjust them to our application scenarios. We systematically test the performance with respect to the hyperparameters of selected ansatzes. Eventually, we construct quantum models with optimized parameters for regression and classification that predict targeted solutes based on the elemental volumes, electronegativities, and bulk moduli of chemical elements. We identify a few combinations of hyperparameters that yield validation scores of approximately 90% for QSVM and hybrid QNN in both tasks.
Quantum steering refers to the phenomenon where one quantum system can instantaneously influence another via local measurements. The critical radius, proposed by Nguyen et al. [Phys. Rev. Lett. 122, 240401 (2019)], is a sufficient-and-necessary steering criterion (SNSC). However, its broad applicability has been restricted by the absence of analytical solutions for general two-qubit states. To address this, we develop the first comprehensive analytical framework for the critical radius, unifying theory and practice. We first derive an exact SNSC for all two-qubit T-states under infinite measurements and verify its equivalence to the critical radius. Leveraging the critical radius's symmetry and concavity, we generalize the framework to arbitrary two-qubit states, aiming to transform the upper and lower bounds of the critical radius into an equal pattern via optimized local operations, thereby deriving an accurate formula for the critical radius. Critically, we discover the first exact analytical SNSCs for two distinct classes of asymmetric states, precisely mapping the boundary between steerable and unsteerable regions. These breakthroughs eliminate reliance on the numerical semidefinite programming methods, provide an operationally efficient method for discovering the hidden steerability.
In the rapidly evolving field of quantum technology, the precise and detailed description of quantum components is not just a necessity but the foundation for advancing research, development, and applications. Optically accessible quantum memories are key building blocks for devices such as quantum repeaters and two-factor authentication. The memory we describe here is based on a Group-IV-vacancy color center () coupled to a highly efficient cavity. It leverages state-dependent reflection from the cavity and implements high-fidelity fractional single qubit gates via a train of optical pulses. We also describe its operation under microwave control, further extending our analysis. Our primary contribution in this work is the integration of this device model into a standardized software framework for quantum memory architectures.
This study formulates a novel, elevation-aware Quadratic Unconstrained Binary Optimization (QUBO) model for multi-vehicle route optimization that simultaneously reduces fuel consumption and traffic congestion. Candidate routes are generated using a gradient-corrected Dijkstra algorithm, and route selection is optimized by minimizing a rigorously constructed QUBO Hamiltonian that incorporates fuel cost, route overlap, and constraint satisfaction. Extensive numerical validation is performed using classical annealing simulations across multiple regions with diverse topographical and road network characteristics, including San Francisco. The results demonstrate that incorporating elevation information significantly reduces fuel consumption, while the proposed overlap penalty effectively mitigates congestion. A clear trade-off between overlap reduction and fuel efficiency is quantitatively characterized. The mathematical consistency of the formulation is ensured through a theoretically derived penalty coefficient, which guarantees constraint satisfaction and stable optimization behavior. Scaling experiments further reveal the limitations of classical solvers as the number of candidate routes increases, highlighting the importance of robust QUBO formulations and motivating future implementation on quantum processing units. Overall, this work establishes a validated and theoretically sound QUBO framework for sustainable transportation optimization and provides a reliable performance baseline for future quantum hardware–based investigations.
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