Guihua Zeng

Continuous-variable quantum key distribution (CVQKD) is a highly promising method for building large-scale quantum networks due to its low cost and high capacity. However, current access networks involve hybrid channels and multiple users, where traditional CVQKD protocols may face limitations in performance. At the same time, as an emerging protocol, quantum key distribution with basis-encoding (BE-QKD) is compatible with traditional CVQKD protocols and has the potential to be applied in CVQKD access networks to provide performance advantages. But the performance of the BE-QKD protocol in current CVQKD access networks has not yet been fully exploited. In this work, we conduct an experimental demonstration of a heterogeneous continuous-variable quantum access network, which is based on the BE-QKD protocol with quadrature phase shift keying (QPSK) modulation and the traditional QPSK-CVQKD protocol. In addition, practical CVQKD access networks may involve different types of transmission links. Therefore, this access network is configured with four users, including two free-space channels and two fiber channels, to verify the feasibility of the proposed heterogeneous quantum access network scheme in a hybrid-channel environment. The experimental results demonstrate that the QPSK-BE-QKD protocol offers potential performance advantages across various channel scenarios in quantum access networks, such as the ability to withstand channel loss and secret key rates. And its compatibility with QPSK-CVQKD is confirmed. The proposed scheme is easy to deploy in existing CVQKD access networks and demonstrates significant performance potential, offering a new implementation paradigm for large-scale access scenarios of CVQKD networks with complex and hybrid channels.

In the field of quantum communication, investigating the practical security of systems is conducive to their deployments in real-world scenarios. In this paper, we identify and experimentally demonstrate a side-channel vulnerability within continuous-variable quantum key distribution (CV-QKD) arising from the zero-order hold (ZOH) effect in digital-to-analog conversion. As digital-to-analog converters (DACs) are indispensable for modulation in CV-QKD transmitters, this leakage constitutes an intrinsic and widespread risk. We show that the ZOH-induced spectral side lobes allow an eavesdropper to extract secret information without disturbing the main signal band. Experimental validation on a CV-QKD platform reveals a leakage of 2.68 Mbit/s against a generated secret key rate of 4.73 Mbit/s. Crucially, we propose a defense strategy that completely eliminates this vulnerability, effectively restoring the system’s implementation security. By resolving this fundamental hardware limitation, our work bridges the gap between theoretical models and practical engineering, paving the way for robust, standardized quantum communication networks.

Sensing networks underpin applications from fundamental physics to real-world engineering. Recently, distributed quantum sensing (DQS) has been investigated to boost the sensing performance, yet current schemes typically rely on entangled probes that are fragile to noise and difficult to scale. Here, we propose a DQS protocol that incorporates a causal-order switch into a cyclic network, enabling a single probe to sequentially query N independent sensors in a coherent superposition or a probabilistic mixture of opposite causal orders. By exploiting the noncommutativity between propagation and sensing processes, our scheme achieves a 1/N^2-scaling precision limit without involving entangled probes. Importantly, our approach utilizes a classical mixture of causal orders rather than a quantum switch, making it more feasible for practical realization. We experimentally implement this scheme for distributed beam tilts sensing in a free-space quantum optical network comprising up to 9 sensors, achieving picoradian-scale precision in estimating tilt angle. Our results demonstrate a robust and scalable DQS protocol that surpasses the conventional 1/N Heisenberg scaling in precision, advancing the practical deployment of quantum sensing networks.

Quantum resources can, in principle, enable Heisenberg-limited (HL) sensing, yet no-go theorems imply that HL scaling is generically unattainable in realistic noisy devices. While quantum error correction (QEC) can suppress noise, its use in quantum sensing is constrained by stringent requirements, including prior noise characterization, restrictive signal-noise compatibility conditions, and measurement-based syndrome extraction with global control. Here we introduce an ICO-based QEC protocol, providing the first application of indefinite causal order (ICO) to QEC. By coherently placing auxiliary controls and noisy evolution in an indefinite causal order, the resulting noncommutative interference enables an auxiliary system to herald and correct errors in real time, thereby circumventing the limitations of conventional QEC and restoring HL scaling. We rigorously establish the protocol for single- and multi-noise scenarios and demonstrate its performance in single-qubit, many-body, and continuous-variable platforms. We further identify regimes in which error correction can be implemented entirely by unitary control, without measurements. Our results reveal ICO as a powerful resource for metrological QEC and provide a broadly applicable framework for noise-resilient quantum information processing.

Ghost imaging leverages a single-pixel detector with no spatial resolution to acquire object echo intensity signals, which are correlated with illumination patterns to reconstruct an image. This architecture inherently mitigates scattering interference between the object and the detector but sensitive to scattering between the light source and the object. To address this challenge, we propose an optical diffraction neural networks (ODNNs) assisted ghost imaging method for imaging through dynamic scattering media. In our scheme, a set of fixed ODNNs, trained on simulated datasets, is incorporated into the experimental optical path to actively correct random distortions induced by dynamic scattering media. Experimental validation using rotating single-layer and double-layer ground glass confirms the feasibility and effectiveness of our approach. Furthermore, our scheme can also be combined with physics-prior-based reconstruction algorithms, enabling high-quality imaging under undersampled conditions. This work demonstrates a novel strategy for imaging through dynamic scattering media, which can be extended to other imaging systems.

The Heisenberg limit is acknowledged as the ultimate precision limit in quantum metrology, traditionally implying that root mean square errors of parameter estimation decrease linearly with the time T of evolution and the number N of quantum gates or probes. However, this conventional perspective fails to interpret recent studies of "super-Heisenberg" scaling, where precision improves faster than linearly with T and N. In this work, we revisit the Heisenberg scaling by leveraging the position-momentum uncertainty relation in parameter space and characterizing precision in terms of the corresponding canonical momentum. This reformulation not only accounts for time and energy resources, but also incorporates underlying resources arising from noncommutativity and quantum superposition. By introducing a generating process with indefinite time direction, which involves noncommutative quantum operations and superposition of time directions, we obtain a quadratic increment in the canonical momentum, thereby achieving a nonlinear-scaling precision limit with respect to T and N. Then we experimentally demonstrate in quantum optical systems that this nonlinear-scaling enhancement can be achieved with a fixed probe energy. Our results provide a deeper insight into the Heisenberg limit in quantum metrology, and shed new light on enhancing precision in practical quantum metrological and sensing tasks.

Dynamic scattering remains a significant challenge to the practical deployment of anti-scattering imaging. Existing methods, such as transmission matrix measurements, iterative wavefront shaping, and optical phase conjugation, depend on a quasi-static assumption, requiring the object and scattering medium to remain stable during a single imaging process to enable one-to-one compensation. However, image reconstruction becomes unattainable when this assumption is violated. Here, we propose a novel imaging strategy that counteracts time-dependent scattering perturbations through a fixed modulation module. This one-to-many compensation mechanism is realized via optical diffraction neural networks (ODNNs) trained on simulated datasets. For the first time, we reveal that its feasibility stems from the optical shower-curtain effect, and its effectiveness typically within 1-2 transport mean free paths. Our approach is not only immune to speckle decorrelation but also leverages it to enhance image quality. ODNNs generalize effectively to real-world scattering medium scenarios via multi-simulated scattering media learning and reconstruct images in real time with light-speed processing. We achieve 80 Hz imaging of moving objects in dynamic scattering media with decorrelation times < 1 ms, demonstrating applicability in large-field-of-view imaging and incoherent illumination scenarios. This work lays the foundation for high-speed, intelligent optical imaging, advancing practical anti-dynamic scattering techniques.