Zhihua Chen

Quantum secure direct communication (QSDC) enables the direct transmission of confidential messages without pre-establishing a secret key. Device independence (DI) can further enhance its practical security against untrusted devices. However, existing DI-QSDC protocols rely on a single Clauser–Horne–Shimony–Holt inequality, which severely limits both the achievable communication distance and the secret message capacity, while the detection loophole remains open in DI-QSDC. To overcome these limitations, we propose a one-sided DI-QSDC protocol based on single-photon sources and heralded entanglement. The protocol combines entanglement distribution, loss-tolerant steering-based security verification, and measurement-DI communication verification and decoding. We employ the Navascués–Pironio–Acín hierarchy based semidefinite programming relaxations to evaluate the secret message capacity. Numerical simulations show that our protocol extends communication distances by factors of 5.6 and 32.2, and improves the practical secure communication efficiency by factors of 1.8 and 1102.5, compared to single-photon-source-based DI-QSDC and SPDC-based DI-QSDC, respectively.

Quantum sensing leverages quantum resources to surpass the standard quantum limit, yet many existing protocols rely on the preparation of complex entangled states and Hamiltonian engineering, posing challenges for universality and scalability. Here, we report an experimental implementation of a universal butterfly metrology protocol, proposed in Kobrin et al. [A universal protocol for quantum-enhanced sensing via information scrambling, arXiv:2411.12794.] demonstrating a scrambling-based approach for quantum-enhanced sensing on a superconducting quantum processor. By exploiting many-body information scrambling, we observe quantum-enhanced phase sensitivity beyond the standard quantum limit, with a scaling consistent with a factor of 2 of the Heisenberg limit for system sizes of up to 10 qubits after a normalization process. Importantly, we experimentally establish a connection between the enhanced sensitivity and the dynamics of the out-of-time-order correlator, and show that the buildup of scrambling-induced genuine multipartite entanglement underlies the observed sensitivity enhancement. Our results demonstrate a scalable approach for quantum-enhanced sensing in interacting many-body quantum systems.

Quantum coherence and entanglement are fundamental resources in quantum technologies, yet their efficient estimation for unknown states by employing minimal resources in experimental settings remains challenging, particularly in high-dimensional systems. We present a machine learning approach based on support vector regression (SVR) that directly estimates the coherence measures and the geometric measure of quantum entanglement using minimal experimental resources. Our method requires only the diagonal entries of the density matrix, along with the traces of the squared and cubed density matrices for quantum coherence, and additionally along with the traces of the squared and cubed reduced density matrix for estimating quantum entanglement. These quantities can be obtained through random measurements or a hybrid quantum-classical framework. This approach significantly reduces the resource overhead compared to quantum state tomography while maintaining high accuracy. {Furthermore, the support vector quantile regression (SVQR) with pinball loss is employed to prevent SVR overestimation. This model not only ensures that over 95\% of predictions are conservative lower bounds in most cases, but also maintains this lower-bound reliability for over 93\% of predictions, despite 2\% perturbations in the input features.} The proposed technique provides a practical and scalable tool for characterizing quantum resources across computation, communication, and metrology applications.

Quantum algorithms have demonstrated provable speedups over classical counterparts, yet establishing a comprehensive theoretical framework to understand the quantum advantage remains a core challenge. In this work, we decode the quantum search advantage by investigating the critical role of quantum state properties in random-walk-based algorithms. We propose three distinct variants of quantum random-walk search algorithms and derive exact analytical expressions for their success probabilities. These probabilities are fundamentally determined by specific initial state properties: the coherence fraction governs the first algorithm's performance, while entanglement and coherence dominate the outcomes of the second and third algorithms, respectively. We show that increased coherence fraction enhances success probability, but greater entanglement and coherence reduce it in the latter two cases. These findings reveal fundamental insights into harnessing quantum properties for advantage and guide algorithm design. Our searches achieve Grover-like speedups and show significant potential for quantum-enhanced machine learning.

Quantum entanglement and coherence are crucial resources in quantum information theory. In some scenarios, however, it is not necessary to directly estimate entanglement or coherence measures to quantify the capabilities of a state in quantum information processing. Instead, fully entangled fraction and coherence fraction are two alternatives for entanglement and coherence in specific quantum tasks. Here, we establish a link between the coherence fraction and the Bernstein-Vazirani algorithm, which has several potential applications including cryptography and database search. We show that the success probability of the generalized Bernstein-Vazirani algorithm depends only on the coherence fraction of the initial state rather than its entanglement or coherence. Moreover, we discuss the coherence fraction dynamics and establish a relation between the operator's coherence fraction and the algorithm's success probability. Our findings highlight how quantum coherence fraction influences the efficiency of quantum algorithms.

In the noisy intermediate‐scale quantum (NISQ) era, multipartite entanglement in realistic noisy environments exhibits complex structures. Characterizing entanglement structures—particularly entanglement depth and intactness—has become increasingly essential for quantum information processing tasks. Traditional methods like standard entanglement witnesses assume that experimenters can exactly implement specific quantum measurements. Even minor inaccuracies in standard witness can introduce significant errors in entanglement detection. Certification methods are proposed with relaxed assumptions about the system and measurements for entanglement structures. First, entanglement states are classified with the same intactness into three distinct types based on their depth. Next, analytical witnesses are derived to detect the entanglement structure of general n‐qubit systems in both device‐independent and semi‐device‐independent scenarios, where some observables are untrusted while others remain reliable. Furthermore, this framework is extended to account for small measurement imperfections, ensuring robustness in practical settings. It is demonstrated that this witness can detect larger entanglement regions compared to previous Bell‐type inequalities. Remarkably, for Type I 4‐partite quantum states, this entanglement witness with measurement imperfections is identical to both device‐dependent and semi‐device‐independent approaches.

Quantum algorithms have demonstrated provable speedups over classical counterparts, yet establishing a comprehensive theoretical framework to understand the quantum advantage remains a core challenge. In this work, we decode the quantum search advantage by investigating the critical role of quantum state properties in random-walk-based algorithms. We propose three distinct variants of quantum random-walk search algorithms and derive exact analytical expressions for their success probabilities. These probabilities are fundamentally determined by specific initial state properties: the coherence fraction governs the first algorithm’s performance, while entanglement and coherence dominate the outcomes of the second and third algorithms, respectively. We show that increased coherence fraction enhances success probability, but greater entanglement and coherence reduce it in the latter two cases. These findings reveal fundamental insights into harnessing quantum properties for advantage and guide algorithm design. Our searches achieve Grover-like speedups and show significant potential for quantum-enhanced machine learning.