Quantum Information Processing最新文献

Continuous-variable measurements cannot select individual outputs as in the discrete case; instead, the possible results are determined with a finite resolution. Then, it is said that continuous-variable measurement devices are insufficiently selective. By utilizing this concept, we show that the probability and fidelity of teleportation in a two-mode continuous-variable cluster state can be handled by both the localization and width of the selectivity interval of the measurement apparatus. Furthermore, we identify a trade-off relationship between the probability and fidelity of teleportation, which depends on both the width of the selectivity interval and the level of squeezing achieved in the cluster. Besides, we provide the mathematical expression for the probability distribution associated with the likelihood of teleportation in the two-mode cluster, which is a fundamental solution of the heat equation. In addition, we show that the fidelity of teleportation in the two-mode cluster is the quotient between the squared solution of a non-homogeneous heat equation and the solution of the conventional heat equation. We extend our approach to a configuration involving successive clusters with intermediate corrections between each teleportation step. To exemplify our proposal, we consider the specific case of a squeezed-coherent state as the quantum state under teleportation.

In this paper, we study 1-generator quasi-twisted (QT) codes with their k-Galois duals. We obtain the generating set of k-Galois dual codes of 1-generator QT codes under some restrictions. Further, we get necessary and sufficient conditions for 1-generator QT codes to be Galois self-orthogonal and Galois self-dual. We prove that Galois self-orthogonal QT codes are asymptotically good. Some optimal and near optimal self-dual 1-generator QT codes are obtained. As an application, we construct some new quantum codes using Galois self-orthogonal 1-generator QT codes.

In [Phys. Rev. A 62, 062314 (2000)], Dür et al. proposed that three qubits can be entangled in two inequivalent ways. That is, the genuinely entangled states of three qubits are partitioned into two SLOCC equivalence classes: GHZ and W. In [M. Walter et al., Science 340, 1205, 7 June (2013)], via polytopes they gave a sufficient condition for genuinely entangled pure states and discussed SLOCC classification. In this paper, we study entanglement classification of pure states of n qubits via the basis state matrix (BSM). We propose the canonical form of BSM, which can be obtained by exchanging columns and rows of BSM. Then, we establish a necessary and sufficient condition for genuinely entangled states of n qubits via the canonical form of BSM. Thus, genuinely entangled states of n qubits can be partitioned into two families. One family includes all states whose BSM cannot be transformed into the canonical form. The states whose BSM cannot be transformed into the canonical form are always genuinely entangled, no matter what the nonzero coefficients are. GHZ and W states belong to this family. The other family includes all states for which BSM can be transformed into the canonical form, but for any canonical form of BSM, some two rows (or columns) of the corresponding coefficient matrix are not proportional. The cluster state belongs to this family.

This work investigates the photonic spin hall shift is controlled within a dielectric medium influenced by the Kerr nonlinearity effect. The incoming light beams interacts with cavity that contains four-level atomic system. The spin hall shift is tuned to exhibit positive or negative values, depending on the parameters of applied driving fields. The spin hall shift reaches its peak value of ( pm 57.2lambda le alpha ^{L,R}_{p} le pm 58lambda ), when influenced by the Rabi frequency of the control field ((zeta _{2})= 8 (Gamma ) and 50 (Gamma )). Conversely, minimum value of the spin hall shift is recorded within the range of ( pm 18.858lambda le alpha ^{L,R}_{p} le pm 18.868lambda ) which is a function of the control field phase ((phi _{2})= 0.4 radian, 2.5 radian and 1.5 radian). These findings have useful applications in sensing technology, quantum information processing and optical communication systems.

Quantum generative adversarial networks (QGANs) have emerged as a promising direction in quantum machine learning, combining the strengths of quantum computing and adversarial training to enable efficient and expressive generative modeling. This survey provides a comprehensive overview of QGAN models, highlighting key advances from theoretical proposals to experimental realizations. We categorize existing QGAN architectures based on their quantum-classical hybrid structures and summarize their applications in fields such as image synthesis, medical data generation, channel prediction, software defect detection, and educational tools. Special attention is given to the integration of QGANs with domain-specific techniques, such as optimization heuristics, Wasserstein distance, variational circuits, and large language models. We also review experimental demonstrations on photonic and ion-trap quantum processors, assessing their feasibility under current hardware limitations. This survey aims to guide future research by outlining existing trends, challenges, and opportunities in developing QGANs for practical quantum advantage.

Quantum audio processing is of great significance for artificial intelligence-driven audio recognition technology. To address traditional deep learning methods’ low efficiency and insufficient temporal modeling capabilities in audio data processing, this paper proposes a quantum time-series encoding (QTSE) method and a quantum audio neural network (QANN). The QTSE method leverages entangled qubit sequences to efficiently encode audio data into quantum states while preserving essential features. Building on QTSE, a quantum audio neural network is further developed and applied to audio recognition and classification tasks through quantum circuit parameter optimization. Experimental results on the GTZAN dataset show that the proposed method achieves a classification accuracy of 75% under limited qubit conditions, outperforming traditional amplitude and angle encoding schemes. Furthermore, the proposed QANN architecture is inherently parameter-efficient, utilizing only 33 trainable parameters, a quantity substantially smaller than classical deep learning counterparts typically used for similar audio classification tasks. This research provides a new pathway for building efficient quantum audio processing technology and demonstrates broad application potential in quantum machine learning.

We study particle and energy transport in an open quantum system consisting of a three-harmonic oscillator chain coupled to thermal baths at different temperatures placed at the ends of the chain. We consider the exact dynamics of the open chain and its so-called local and global Markovian approximations. By comparing them, we show that, while all three yield a divergence-like continuity equation for the probability flow, the energy flow exhibits instead a distinct behavior. The exact dynamics and the local one preserve a standard divergence form for the energy transport, whereas the global open dynamics, due to the rotating wave approximation (RWA), introduces non-divergence sink/source terms. These terms also affect the continuity equation in the case of a master equation obtained through a time-coarse-graining method whereby RWA is avoided through a time-zoom parameter (Delta t). In such a scenario, sink and source contributions are always present for each (Delta t>0). While in the limit (Delta trightarrow +infty ) one recovers the global dissipative dynamics, sink and source terms instead vanish when (Delta trightarrow 0), restoring the divergence structure of the exact dynamics. Our results underscore how the choice of the dissipative Markovian approximation to an open system dynamics critically influences the energy transport descriptions, with implications for discriminating among them and thus, ultimately, for the correct modeling of the time-evolution of open quantum many-body systems.

To address the challenge of balancing limited quantum capabilities on the user side with communication efficiency in semi-quantum cryptography, this work introduces polarization and spatial-mode degrees of freedom (PSDF) single photons technology into the semi-quantum dialogue (SQD) framework and proposes a novel SQD protocol. This protocol employs parallel encoding across the dual degrees of freedom (DoFs) of single photons to achieve bidirectional secure communication, requiring only a single round of security verification. As the classical participant, Bob encodes his secret information via (Z_{P} otimes Z_{S}) basis measurements and re-preparation of states, returning quantum sequence to Alice. Alice decodes Bob's message based on her knowledge of the initial states and encodes her own secret information by applying specific unitary operations. In terms of security, the protocol effectively resists various active eavesdropping attacks. Furthermore, it incorporates one-way hash functions and error correction coding mechanisms to enhance robustness in noisy environments. The proposed scheme utilizes PSDF single photons product states as its quantum resource, whose preparation and measurement can be realized with existing mature experimental techniques, ensuring high practical feasibility. This work provides a new perspective for the practical development of semi-quantum communication systems.

State vector simulation is useful for designing and analyzing quantum algorithms. The challenge is that the size of the state vector increases exponentially with the number of quantum bits (qubits) and the entire state vector should be updated when simulating each quantum gate. Gate fusion, a circuit minimization technique, helps reduce simulation time by combining multiple quantum gates into one. However, the gate matrix will be large if the fused gate acts on too many qubits, which may increase the simulation costs. Previous work limits the size of fused gates based on the number of input qubits, but the impact of qubits’ types is neglected. This paper proposes a novel two-stage gate fusion strategy, namely QMin, based on the observation that control input qubits can reduce the simulation cost of a gate, which has not been discussed before. Specifically, QMin designs a pattern-controlled logic gate structure to fuse target gates apart from their control qubits. For the first target-oriented fusion stage, QMin defines beneficial mergeable gate types based on the required multiplication operations. The second tensor-oriented fusion stage merges gates under a constraint on the gate size to further reduce the number of gates. Experimental results on various circuits show that QMin can achieve more than 2.03 times speedup on average in total execution time compared with previous methods.

The celebrated Hong–Ou–Mandel effect illustrates the richness of two-photon interferometry. In this work, we demonstrate that this extends to the realm of time–frequency interferometry. Taking advantage of the mathematical analogy which can be drawn between the frequency and quadrature degrees of freedom of light when there is a single photon in each auxiliary mode, we consider the equivalent of the Hong–Ou–Mandel effect in the frequency domain. In this setting, the n-Fock state becomes equivalent to a single-photon state with a spectral wave function given by the (n^{th}) Hermite–Gauss function and destructive interference corresponds to vanishing probability of detecting single photons with an order one Hermite–Gauss spectral profile. This compelling analogy motivates us to propose an interferometric strategy that uses a frequency-engineered two-photon state to achieve enhanced phase precision that scales inversely with the number of modes. Finally, we generalize the Gaussian Boson sampling model to time–frequency degrees of freedom of single photons. Through all these applications, we emphasize that distinct types of quantum resources and degrees of freedom can yield identical statistical outcomes and information processing capabilities.