EPJ Quantum Technology最新文献

The practical deployment of Continuous Variables Quantum Key Distribution (CV-QKD) systems benefits from existing optical fiber telecommunication infrastructures. However, optical fibers introduce random variations in the state of polarization, which degrades the system’s performance. We consider a CV-QKD system featuring a polarization diversity heterodyne receiver and the constant modulus algorithm (CMA) to compensate for the polarization drifts in the quantum channel. Our setup can effectively realign Alice’s quantum signal with Bob’s local oscillator for polarization drift variances below 10⁻¹⁰. This value is compatible with most experimental implementations, allowing for accurate estimation of the channel transmission and excess noise parameters. Our results establish operational limits for passive polarization drift compensation using a polarization diversity receiver combined with digital CMA, validating its use to compensate for the polarization drift in real-world implementations approximating the ideal scenario of no polarization drift, for polarization drift variances below 10⁻¹⁰. This enables long-term stability in CV-QKD systems, eliminating the need for active polarization controllers and manual adjustments.

Design space exploration (DSE) plays an important role in optimising quantum circuit execution by systematically evaluating different configurations of compilation strategies and hardware settings. In this paper, we conduct a comprehensive investigation into the impact of various layout methods, qubit routing techniques, and optimisation levels, as well as device-specific properties such as different variants and strengths of noise and imperfections, the topological structure of qubits, connectivity densities, and back-end sizes. By spanning through these dimensions, we aim to understand the interplay between compilation choices and hardware characteristics. A key question driving our exploration is whether the optimal selection of device parameters, mapping techniques, comprising of initial layout strategies and routing heuristics can mitigate device induced errors beyond standard error mitigation approaches. Our results show that carefully selecting software strategies (e.g., mapping and routing algorithms) and tailoring hardware characteristics (such as minimising noise and leveraging topology and connectivity density) significantly improve the fidelity of circuit execution outcomes, and thus the expected correctness or success probability of the computational result. We provide estimates based on key metrics such as circuit depth, gate count and expected fidelity. Our results highlight the importance of hardware–software co-design, particularly as quantum systems scale to larger dimensions, and along the way towards fully error corrected quantum systems: Our study is based on computationally noisy simulations, but considers various implementations of quantum error correction (QEC) using the same approach as for other algorithms. The observed sensitivity of circuit fidelity to noise and connectivity suggests that co-design principles will be equally critical when integrating QEC in future systems. Our exploration provides practical guidelines for co-optimising physical mapping, qubit routing, and hardware configurations in realistic quantum computing scenarios.

Quantum Generative Adversarial Networks (QGANs), as a rising paradigm in Quantum Machine Learning, have shown promising potential in image generation and processing. However, their output quality remains suboptimal, and existing research is largely limited to small-scale, proof-of-concept studies. In this work, we propose a hybrid quantum-classical GAN architecture, where the generator integrates parameterized quantum circuits (PQCs) and classical neural networks. This integration significantly enhances the visual quality of generated images. Our model leverages the complementary strengths of quantum and classical components and outperforms existing methods (Tsang et al. in IEEE Trans. Quantum Eng. 4:1–19, 2023; Gulrajani et al. in Proceedings of the 31st international conference on neural information processing systems. NIPS’17, Red Hook, pp. 5769–5779, 2017), particularly in terms of image fidelity. Experiments conducted on the MNIST family of datasets show that our hybrid approach achieves a 20.26% average reduction in Fréchet Inception Distance. Furthermore, it improves the Structural Similarity Index Measure, Cosine Similarity, and Peak Signal-to-Noise Ratio by 26.04%, 2.22%, and 7.62%, respectively. These results highlight the effectiveness of combining quantum computing with machine learning, and underscore the potential of hybrid quantum-classical models in advancing generative tasks.

Tian et al. (EPJ Quantum Technol 11(1):35, 2024) proposed three semi-quantum summation protocols that enable resource-limited participants to securely compute the sum of their private inputs with assistance from a semi-honest third party (TP). In these protocols, participants do not need quantum measurement devices to achieve secure computation. Although the protocols have been analyzed against common attacks to demonstrate their robustness, this study identifies two significant issues in Tian et al.’s work: a security loophole and a design flaw. The security loophole permits a malicious participant to steal another participant’s private input without detection, while the design flaw prevents participants from obtaining the correct summation result when the number of participants is odd. To address these issues, this study proposes improved semi-quantum summation protocols under the same assumptions and environment.

Generating a secure key and securely communicating it are crucial aspects for ensuring information security during encryption and decryption processes. Quantum Key Distribution (QKD) is a promising technique for enabling secure communication in Industrial Internet of Things (IIoT) applications. This paper presents an enhanced BB84 protocol integrated with Elliptic Curve Cryptography (ECC) that improves efficiency, security, and practical implementation. Our enhanced BB84 protocol employs a basis reconciliation mechanism and introduces a depolarizing channel model to simulate realistic noise conditions and eavesdropping detections. The system effectively identifies potential eavesdroppers based on Quantum Bit Error Rate (QBER) thresholds, thereby ensuring a secure key exchange process. Unlike traditional ECC implementations, our approach dynamically extracts prime numbers from a sifted key to generate elliptic curve parameters. The extracted key is used for AES encryption, providing an additional security layer for data confidentiality. The performance evaluation demonstrates efficient key generation and computational time, making this approach practical for IIoT environments. The experimental results indicate successful key generation and privacy amplification with a final key derived from the matched measurement bases. Elliptic curve generation successfully computes valid points supporting secure cryptographic operations. The estimated QBER ranged from 0.0 to 0.25, ensuring a secure key exchange. The AES encryption and decryption processes validate the usability of the generated key in real-world applications, confirming the robustness of our integrated QKD-ECC framework. The average key generation time ranged from 0.0000297 s, while the computational time was 0.0000714 s.

Quantum Anonymous Multi-party Ranking (QAMR) enables quantum ranking operations to be performed on private datasets while concealing associations between participants and their private data. However, most existing protocols rely on semi-honest third parties and require the use of a large number of quantum states to ensure identity anonymity. Furthermore, they lack transferability due to inadequate modular design. To address these issues, a novel QAMR protocol is proposed, which eliminates the need for semi-honest third parties for the first time. The protocol achieves reduced quantum resource consumption while ensuring transferability. Moreover, the protocol’s correctness is proven by theoretical analysis, and its feasibility is confirmed via IBM Qiskit simulations. A thorough security analysis shows that the protocol is resistant to collusion, entanglement-measurement, and intercept-resend attacks. Besides, the comparison shows that the suggested approach uses fewer quantum resources while processing datasets with wider distributions and higher limits.

The rapid accumulation of space debris in Low Earth Orbit (LEO) poses a significant challenge to the sustainability of space operations. While preventive measures limit new debris generation, they are insufficient to mitigate the growing population of defunct satellites, rocket stages, and collision fragments. Active Debris Removal (ADR) has emerged as a viable solution, which requires solving NP-hard combinatorial optimization problem similar to the Traveling Salesman Problem (TSP) to maximize mission efficiency by minimizing fuel and mission duration. This work explores the application of Quantum Annealing (QA) and Hybrid Quantum Annealing (HQA) for optimizing multi-target ADR missions. Specifically, it introduces a Quadratic Unconstrained Binary Optimization (QUBO) model for ADR, exploiting quantum computing to enhance solution efficiency. A novel quadratization method is developed to reduce computational complexity, enabling large-scale mission planning. Additionally, a novel constraint-handling strategy is proposed, integrating mission constraints into post-processing to enhance quantum solver efficiency. The proposed approach is validated using real-world satellite debris datasets and benchmarked against classical metaheuristic optimizers, including Simulated Annealing (SA), Tabu Search (TS), and Genetic Algorithms (GA). The obtained results demonstrate the advantages of quantum optimization for ADR mission planning, providing a scalable and computationally efficient solution.

We propose a hybrid photonic molecule system, which includes a two-level system coupled to two optical cavities and the two cavities interact with each other by a phase-dependent photon-photon interaction. The absorption spectra of the two-level system manifest one or two transparent windows (zero absorption deeps) by the dark-mode effect or by breaking the dark-mode effect, which is accompanied by the rapid dispersion leading to the fast or slow light propagation effect. Combining the phased-dependent photon-photon coupling with the interactions between the two-level system and two optical cavities, the dark-mode effect is controllable due to the quantum interference effect, which together determine the process form fast to slow light effect. Moreover, we consider one optical cavity is loss and the other one can be loss, neutral, or gain. The manipulation and periodic switching of group index can be achieved by tuning the modulation phase, and the fast- and slow-light effects are particularly pronounced in the scenario of one optical cavity is active (gain), compared to those are loss or neutral. This study lays the foundation for the application of photon-mediated optical information storage and processing.

Integrating IoT into daily life generates massive data, enabling smart factories and driving advancements in related technologies like cloud/edge computing, ML, and AI. While ML has been used for data analysis and forecasting, challenges such as data complexity, security, and computing limitations persist, particularly in anomaly detection crucial for network security. Recent research indicates the potential of quantum computing and Quantum Machine Learning (QML) to outperform traditional methods in anomaly detection within IoT, an area lacking a comprehensive review. This paper presents a systematic review of Machine Learning-based anomaly detection techniques for IoT security. Despite previous reviews, this study includes the analysis of feature engineering and quantum machine learning techniques in literature. Our findings show that current models have high detection rates on known datasets, but face scalability, real-time processing, and generalization issues. Privacy and security concerns in federated learning (FL) and the effects of data drift also need to be addressed, along with the challenges of 5G and 6G-enabled IoT environments. Future directions include integrating Explainable AI into anomaly detection, exploring adaptive learning techniques, and combining blockchain with machine learning models. The study also highlights the potential of quantum computing to enhance threat detection through quantum machine learning models.

We present a novel concept for a compact and robust crossed-beam optical dipole trap (cODT) based on a single lens, designed for the efficient generation of Bose-Einstein condensates (BECs) under dynamic conditions. The system employs two independent two-dimensional acousto-optical deflectors (AODs) in combination with a single high-numerical-aperture lens to provide full three-dimensional control over the trap geometry, minimizing potential misalignments and ensuring long-term operational stability. By leveraging time-averaged potentials, rapid and efficient evaporative cooling sequences toward BECs are enabled. The functionality of the cODT under microgravity conditions has been successfully demonstrated in the Einstein-Elevator in Hannover, Germany, where the beam intersection was shown to remain stable throughout the microgravity phase of the flight. In addition, the system has been implemented in the sensor head of the INTENTAS project to verify BEC generation. Additional realization of one-, two-, and three-dimensional arrays of condensates through dynamic trap shaping was achieved. This versatile approach allows for advanced quantum sensing applications in mobile and space-based environments based on all-optical BECs.