EPJ Quantum Technology最新文献

AC susceptometry, unlike static susceptometry, offers a deeper insight into magnetic materials. By employing AC susceptibility measurements, one can glean into crucial details regarding magnetic dynamics. Nevertheless, traditional AC susceptometers are constrained to measuring changes in magnetic moments within the range of a few nano-joules per tesla. Additionally, their spatial resolution is severely limited, confining their application to bulk samples only. In this study, we introduce the utilization of a Nitrogen Vacancy (NV) center-based quantum diamond microscope for mapping the magnetic fields resulting from micron-scale ferromagnetic samples under an AC drive field, which can be used for determining AC susceptibility with sufficient additional information about the sample. By employing coherent pulse sequences, we extract the in-phase component of the sample magnetic field from samples within a field of view spanning 70 micro-meters while achieving a resolution of 1 micro-meter. Furthermore, we quantify changes in dipole moment on the order of a femto-joules per tesla induced by excitations at frequencies reaching several hundred kilohertz.

Advancements in quantum computing pose a significant threat to most of the cryptography currently deployed in our communication networks. Fortunately, cryptographic building blocks to mitigate this threat are already available; mostly based on Post-Quantum Cryptography (PQC) and Quantum Key Distribution (QKD), but also on symmetric cryptography techniques. Notably, those building blocks must be deployed as soon as possible in communication networks due to the “harvest-now decrypt-later” attack scenario, which is already challenging our sensitive and encrypted data today.

Following an agile and defense-in-depth approach, Hybrid Authenticated Key-Exchange (HAKE) protocols have recently been gaining significant attention. Such protocols have the benefit of modularly combining classical (symmetric) cryptography, PQC, and QKD to achieve strong confidentiality, authenticity, and integrity guarantees for network channels. Unfortunately, only a few protocols have yet been proposed (mainly Muckle and Muckle+) with different flexibility guarantees.

Looking at available standards in the network domain – especially at the Media Access Control Security (MACsec) standard – we believe that HAKE protocols could already bring strong security benefits to MACsec today. MACsec is a standard designed to secure communication at the data link layer in Ethernet networks by providing confidentiality, authenticity, and integrity for all traffic between trusted nodes. In addition, it establishes secure channels within a Local Area Network (LAN), ensuring that data remain protected from eavesdropping, tampering, and unauthorized access, while operating transparently to higher layer protocols. Currently, MACsec does not offer enough protection against the aforementioned threats.

In this work, we tackle the challenge and propose a new versatile HAKE protocol, dubbed VMuckle, which is sufficiently flexible for use in MACsec. The use of VMuckle in MACsec provides LAN participants with quantum-safe hybrid key material to ensure secure communication even in the event of cryptographically relevant quantum computers.

While adders are required for many classical and quantum algorithms, nowadays’ single quantum computer implementations cannot handle the large qubit counts required in practical applications. Implementing a distributed approach is currently the only solution, but it poses the challenge of communication latency. This paper introduces a quantum distributed adder algorithm (QUDA) as a solution for many applications that require large qubit counts. QUDA offers a logarithmic number of instances of quantum data transfer for the addition of two numbers in comparison with existing solutions which are generally either based on ripple carry adders with a linear number of transmission rounds or attempt to distribute an existing monolithic circuit without specializing their techniques to adders. We include implementation details and the used testing methodology, showcasing the correctness and efficiency of the proposed algorithm.

In recent years, in the context of the rapid development of quantum computing technology, quantum attack methods such as Shor’s algorithm pose a serious threat to the traditional public key encryption system based on number-theoretic puzzles. Using the characteristics of quantum bits, this paper proposes a quantum grey-scale image encryption method based on alternating quantum random walk. Firstly, the quantum representation model is used to transform the image into a quantum state, and then the quantum key is generated by the alternating quantum random walk algorithm, and combined with the quantum gate operation for encrypting the grey-scale image data, which not only inherits the advantage of the anti-attack of the quantum computation, but also, through the quantum parallelism and the non-clonability, which solves the security and efficiency bottleneck of traditional image encryption in the quantum era and significantly improves the security of grey-scale image encryption. The algorithm proposed in this paper has been verified by simulation experiments, and the experimental results show that the method is excellent in encryption and decryption effects, and for the encrypted image, a number of performance analyses have been carried out, and the analysis results show that the proposed encryption method has a high degree of security, and it can effectively resist the statistical attack, noise attack, etc., and the distribution of the histogram of encrypted image is more uniform, the pixel correlation analysis is close to 1, and the information entropy is close to 7.999.

Various architectures have been proposed using a large array of semiconductor spin qubits with high-fidelity and high-speed gate operation. However, no quantum algorithm compilers have been developed which can compile quantum algorithms in a consistent manner for the various architectures, limiting the discussion on evaluating the efficiency of quantum algorithm implementation. Here, we propose Qubit Operation Orchestrator considering qubit Connectivity and Addressability Implementation (QOOCAI), a first quantum algorithm compiler designed for various architectures with semiconductor spin qubits. QOOCAI can compile quantum algorithms to various architectures with different qubit connectivity and addressability, which are important features that affect the efficiency of quantum algorithm implementation. Furthermore, we compile multiple quantum algorithms on different architectures with QOOCAI, showing that higher qubit connectivity and addressability make the algorithm implementation quantitatively more efficient. These findings are crucial for developing semiconductor spin qubit devices, highlighting QOOCAI’s potential for improving quantum algorithm implementation efficiency across diverse architectures.

Accurate amine property prediction is essential for optimizing CO₂ capture efficiency in post-combustion processes. Quantum machine learning (QML) can enhance predictive modeling by leveraging superposition, entanglement, and interference to capture complex correlations. In this study, we developed hybrid quantum neural networks (HQNN) to improve quantitative structure-property relationship (QSPR) modeling for CO₂-capturing amines. By integrating variational quantum regressors with classical multi-layer perceptrons and graph neural networks, quantum-enhanced performance was explored in physicochemical property prediction under noiseless conditions and robustness was evaluated against quantum hardware noise using IBM quantum systems. Our results showed that HQNNs improve predictive accuracy for key solvent properties, including basicity, viscosity, boiling point, melting point, and vapor pressure. The fine-tuned and frozen pre-trained HQNN models with 9 qubits consistently achieved the highest rankings, highlighting the benefits of integrating quantum layers with pre-trained classical models. Furthermore, simulations under hardware noise confirmed the robustness of HQNNs, maintaining predictive performance. Overall, these findings emphasize the potential of hybrid quantum-classical architectures in molecular modeling. As quantum hardware and QML algorithms continue to advance, practical quantum benefits in QSPR modeling and materials discovery are expected to become increasingly attainable, driven by improvements in quantum circuit design, noise mitigation, and scalable architectures.

Quantum Machine Learning (QML) has attracted significant attention for its potential to deliver exponential advantages over classical machine learning approaches, particularly in classification and recognition tasks. Quantum Generative Adversarial Networks (QGANs), a form of quantum machine learning, provide promising advantages in image processing and generation tasks when compared to classical technologies. However, the limitations of current quantum devices have led to suboptimal image quality and limited robustness in earlier methods. To overcome these challenges, we developed a hybrid quantum-classical approach, introducing CAQ, a quantum-classical Generative Adversarial Network (GAN) framework. Leveraging the latest WGAN-gradient penalty (GP) strategy, we trained and optimized the quantum generator, reduced the complexity of parameters, and implemented an adaptive noise input system that dynamically adjusts noise levels, thereby improving the model’s robustness. Additionally, we employed a remapping technique to transform the original image’s multimodal distribution into a unimodal one, thereby reducing the complexity of the learned distribution. Experiments on MNIST and Fashion-MNIST datasets show that CAQ generates grayscale images effectively, demonstrating its feasibility on near-term intermediate-scale quantum (NISQ) computers.

The Deep Space Quantum Link (DSQL) is a space-mission concept that aims to explore the interplay between general relativity and quantum mechanics using quantum optical interferometry. This mission concept was formally presented to the United States National Academy of Science Decadal Survey as a research campaign for Fundamental Physics in 2022. Since then, advances have been made in the space-based quantum optical technologies required to conduct a DSQL-type mission. In addition, other research efforts have defined alternative measurement concepts to explore the same scientific questions motivating the DSQL mission. This paper serves as an update to the community on the status of the DSQL mission concept and related research and technology development efforts.

The rise of advanced networking and mobile technologies has improved flexibility in Software Defined Networking (SDN) management and mobile ecosystems but it has also introduced vulnerabilities like Distributed Denial of Service (DDoS) attacks and Android malware. In this research, we propose a Hybrid Quantum Classical Neural Network (HQCNN) framework that operates with a Dressed Quantum Circuit (DQC) to achieve efficient detection and classification of threats. The input pipeline of the HQCNN integrates Wavelet Transforms based feature pre-processing, Convolutional Neural Network based feature extraction, Linear Discriminant Analysis (LDA) for dimensionality reduction, and quantum layers for enhanced classification with less computational complexity. Experiments were conducted on the SDN DDoS Attack Dataset and the CCCS-CIC-AndMal2020 Static Dataset. Two different model variants were devised for binary and multiclass classification problems addressing various cybersecurity issues. The binary HQCNN model for SDN-based DDoS detection was implemented on AWS Braket’s real Quantum Processing Unit (QPU), achieving 99.86% accuracy, 99.85% precision, 100% recall, and a 99.88% F1-score, thereby outperforming the classical Convolutional Neural Network (CNN). The multiclass HQCNN, on the other hand, attains accuracy of 93.56%, 94.38%, and 95.13% on the 15-class, 14-class, and 12-class versions of CCCS-CIC-AndMal2020 Static, respectively, hence outperforms all existing methods. These results show that HQCNN is efficient, scalable, and very much applicable in cybersecurity, validating its real-world use effectiveness applicability in threat detection.

To address qubits’ high environmental sensitivity and reduce the significant error rates in current quantum devices, quantum error correction stands as one of the most dependable approaches. The topological surface code, renowned for its unique qubit lattice structure, is widely considered a pivotal tool for enabling fault-tolerant quantum computation. Through redundancy introduced across multiple qubits, the surface code safeguards quantum information and identifies errors via state changes captured by syndrome qubits. However, simultaneous errors in data and syndrome qubits substantially escalate decoding complexity. Quantum Generative Adversarial Networks (QGANs) have emerged as promising deep learning frameworks, effectively harnessing quantum advantages for practical tasks such as image processing and data optimization. Consequently, a topological code trainer for quantum-classical hybrid GANs is proposed as an auxiliary model to enhance error correction in machine learning-based decoders, demonstrating significantly improved training accuracy compared to the traditional Minimum Weight Perfect Matching (MWPM) algorithm, which achieves an accuracy of 65%. Numerical experiments reveal that the decoder achieves a fidelity threshold of P = 0.1978, substantially surpassing the traditional algorithm’s threshold of P = 0.1024. To enhance decoding efficiency, a Transformer decoder is integrated, incorporating syndrome error outputs trained via QGANs into its framework. By leveraging its self-attention mechanism, the Transformer effectively captures long-range qubit dependencies at a global scale, enabling high-fidelity error correction over larger dimensions. Numerical validation of the surface code error threshold demonstrates an 8.5% threshold with a correction success rate exceeding 94%, whereas the local MWPM decoder achieves only 55% and fails to support large-scale computation at a 4% threshold.