Advanced quantum technologies最新文献

A quantum-enhanced scheme for microwave (MW) electric (E) field detection is theoretically proposed, utilizing transient amplification without inversion (AWI) in cold ⁸⁷Rb atoms. It is achieved by modulating a continuous-wave coupling laser into a periodic near-square-wave pulse, which enables precise control over quantum coherence and induces transient gain. Simulation results demonstrate significant performance enhancements of the AWI scheme compared to conventional electromagnetically induced transparency (EIT) approaches. In comparison, the AWI scheme shows 7.3 times enhanced transient transmission intensity. The reduction of the full width at half maximum from 0.54 × 2π to 0.15 × 2π MHz represents a 72% narrowing of the spectral linewidth, which enables superior spectral resolution and extends the range of measurable Autler-Townes splitting. Furthermore, the minimum detectable MW E-field strength of 172.8 nV cm⁻¹ represents an order of magnitude improvement. The robustness of the AWI signal against simulated technical noise is also confirmed. The demonstrated capabilities establish AWI as a promising technique for high-sensitivity quantum MW electrometry.

We propose a novel quantum digital signature protocol that eliminates the need for a trusted third-party, a common limitation in existing quantum signature schemes. While the concept of third-party-free quantum signatures has been discussed in earlier works, such as Gottesman and Chuang (2001), no concrete protocol with provable information-theoretic security has been presented to date. Recent studies have explored computationally secure quantum signature schemes without trusted parties, but their security relies on assumptions about quantum computational hardness. In contrast, our protocol achieves information-theoretic unforgeability based solely on the non-cloning property of quantum states. It uses classical private keys and quantum public keys, and requires only single-qubit operations. The scheme also satisfies key security properties, including asymmetry, undeniability, and expandability, making it suitable for implementation in near-term quantum technologies.

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.

State-of-the-art free-space continuous-variable quantum key distribution (CV-QKD) protocols use phase reference pulses to modulate the wavefront of a real local oscillator at the receiver, thereby compensating for wavefront distortions caused by atmospheric turbulence. It is normally assumed that the wavefront distortion in the phase reference pulses is identical to the wavefront distortion in the quantum signals, which are multiplexed during transmission. However, in real-world deployments, there can exist a relative wavefront error (WFE) between the reference pulses and quantum signals, which, among other deleterious effects, can severely limit secure key transfer in satellite-to-Earth CV-QKD. In this work, we introduce machine learning-based wavefront correction algorithms, which utilize multi-plane light conversion for decomposition of the reference pulses and quantum signals into the Hermite-Gaussian (HG) basis, then estimate the difference in HG mode phase measurements. Through detailed simulations of the Earth-satellite channel, we demonstrate that our algorithm can identify and compensate for any relative WFEs that may exist. We quantify the gains available in our algorithm in terms of the CV-QKD secure key rate. We show channels where positive secure key rates are obtained using our algorithms, while information loss without wavefront correction would result in null key rates.

We investigate how parity-time ($�\mathrm{PT}$) symmetry influences photon blockade in an atom-coupled dual-cavity quantum electrodynamics (QED) system, with a focus on distinguishing the underlying mechanisms. Statistical analysis demonstrates that photon blockade exhibits qualitatively distinct behaviors in the $�\mathrm{PT}$-symmetric and symmetry-broken phases, thereby providing a clear signature of the $�\mathrm{PT}$ phase transition. In this $�\mathrm{PT}$-symmetric structure, the two-level atom provides the required nonlinearity, while cavity-cavity coupling under $�\mathrm{PT}$-symmetric control cooperatively enhances photon antibunching, leading to simultaneous photon blockade in both the passive and the active cavities. These phenomena are comprehensively analyzed using both analytical solutions of the Schrödinger equation and numerical simulations of the master equation. Comparisons with non-$�\mathrm{PT}$-symmetric configurations reveal that $�\mathrm{PT}$ symmetry significantly enhances photon antibunching, mean photon number and promotes cooperative blockade behavior across both cavities. In contrast to conventional photon blockade schemes, our approach remains effective under weak coupling and weak nonlinearity conditions, offering a robust and tunable pathway toward realizing high-performance single-photon sources in non-Hermitian quantum systems.

Variational quantum algorithms (VQAs) have emerged as a promising approach for achieving quantum advantage on current noisy intermediate-scale quantum devices. However, their large-scale applications are significantly hindered by optimization challenges, such as the barren plateau (BP) phenomenon, local minima, and numerous iteration demands. In this work, we leverage denoising diffusion models (DM) to address these difficulties. The DM is trained on a few data points in the Heisenberg model parameter space and then can be guided to generate high-performance parameters for parameterized quantum circuits (PQCs) in variational quantum eigensolver (VQE) tasks for general Hamiltonians. Numerical experiments demonstrate that DM-parameterized VQE can explore the ground-state energies of Heisenberg models with parameters not included in the training dataset. Even when applied to previously unseen Hamiltonians, such as the Ising and Hubbard models, it can generate the appropriate initial state to achieve rapid convergence and mitigate the BP and local minima problems. More interestingly, we discover the possibility of parameter transferability and extrapolation among different quantum many-body Hamiltonians.