EPJ Quantum Technology最新文献

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.

Quantum dot intermediate band solar cells (QD-IBSCs) have attracted significant attention as a promising approach to enhance solar cell efficiency by two-step two-photon absorption. The Shockley-Queisser limitation has been resolved by using QD-IBSCs, which was a challenge for solar cell commercialization. In this study, we employed an efficient approach in QD-IBSCs to enhance the solar cell efficiency by using the truncated conical quantum dot (TCQD) shape. The effect on the performance of TCQD-IBSC has been symmetrically examined by varying the geometrical parameters, band gap, electron affinity, doping concentration, absorber layer thickness, and carrier mobility. Interestingly, TCQD-IBSC showed an efficiency of 51.1%, which decreases to 12.3%, 14.1%, and 26% with the increase in bandgap, doping concentration, and electron affinity, respectively. Notably, we improved the short-circuit current density by increasing the thickness of the absorber layer to 330 nm and carrier mobility to 4000 cm²V⁻¹s⁻¹, which led to higher power conversion efficiencies (PCE) of the solar cell. Moreover, a trade-off relation has been observed between QD size and interdot spacing. The PCE is gradually decreased from 49 % to 41.4 % with the increase in temperature. This model structure provides a new direction toward the achievement of high-efficiency TCQD-IBSCs and may promote the development of next-generation solar cells with high efficiency.

With the ongoing advances in noisy intermediate-scale quantum hardware, variational quantum algorithms have demonstrated significant potential in a range of quantum applications. However, obtaining high-performance, shallow-parameterized quantum circuits typically requires repeated optimization of the gate parameters over a large set of candidate circuits, resulting in prohibitively high evaluation costs. To address this challenge, this study proposes a novel predictor-based quantum architecture search (PQAS-ZX) method that leverages ZX-calculus. In this approach, a quantum circuit is first represented as a ZX diagram that supports multi-step equivalent simplifications at the diagram level. By applying these equivalence transformations, multiple circuit variants that share the same performance metric are generated, thereby significantly expanding the training dataset and enhancing the ability of the predictor to manage diverse circuit structures. ZX diagrams offer more flexible characterizations of multi-qubit entanglement and phase interactions, as well as higher-level equivalent transformations, compared with the state-of-the-art predictor-based quantum architecture search with graph measures (PQAS-GM). Numerical simulations of three variational quantum eigensolver tasks, namely the transverse-field Ising, Heisenberg, and BeH₂ molecular models, demonstrated that PQAS-ZX required only approximately 80.9%, 82.9%, and 76.1% of the queries required by PQAS-GM, respectively, to achieve the same probability of reaching the target ground-state energy. These results highlight the advantage of using ZX diagrams to identify high-quality circuits efficiently and alleviate the evaluation burden of quantum architecture searches.

Background

Introducing advanced quantum mechanics (QM) and quantum technology (QT) concepts to high school students is a global priority aimed at developing a quantum-literate workforce for the growing QT industry. However, high school-initiated QT outreach programs embedded in sustainable, school-led activities remain rare, with most researcher-led programs treating classroom integration as an afterthought. This study addresses this gap by reporting findings from a school-initiated, fully online quantum education STEM & Research Internship Program (SRIP) for Filipino high school students.

Method and Theoretical Framework

We employed a single-group quasi-experimental pre-post research design, collecting data via a mixed-methods approach using validated concept inventories and students’ daily journal entries. The program was guided by a theoretical framework integrating the discipline–culture paradigm of physics knowledge (for curriculum design) with the cognitive apprenticeship model (for curriculum implementation). Twenty high-achieving students (11 males, 9 females; Grades 9–11) from a STEM-focused Philippine high school participated.

Results and Conclusion

Results indicate increased knowledge of QM and QT concepts and improved attitudes towards QM among students following completion of the quantum education SRIP. Findings highlight the program’s positive educational impact and its novelty as the first school-initiated, fully online quantum outreach initiative in the Philippines, with potential for global adoption.