EPJ Quantum Technology最新文献

{"title":"Quantum algorithm compiler for architectures with semiconductor spin qubits","authors":"Masahiro Tadokoro,&nbsp;Ryutaro Matsuoka,&nbsp;Tetsuo Kodera","doi":"10.1140/epjqt/s40507-025-00384-9","DOIUrl":"10.1140/epjqt/s40507-025-00384-9","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":547,"journal":{"name":"EPJ Quantum Technology","volume":"12 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://epjquantumtechnology.springeropen.com/counter/pdf/10.1140/epjqt/s40507-025-00384-9","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145141896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hybrid quantum neural networks with variational quantum regressor for enhancing QSPR modeling of CO2-capturing amine","authors":"Hyein Cho,&nbsp;Jeonghoon Kim,&nbsp;Kyoung Tai No,&nbsp;Hocheol Lim","doi":"10.1140/epjqt/s40507-025-00385-8","DOIUrl":"10.1140/epjqt/s40507-025-00385-8","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":547,"journal":{"name":"EPJ Quantum Technology","volume":"12 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://epjquantumtechnology.springeropen.com/counter/pdf/10.1140/epjqt/s40507-025-00385-8","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145144290","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantum generative adversarial network with automated noise suppression mechanism based on WGAN-GP","authors":"Yanbing Tian,&nbsp;Cewen Tian,&nbsp;Zaixu Fan,&nbsp;Minghao Fu,&nbsp;Hongyang Ma","doi":"10.1140/epjqt/s40507-025-00372-z","DOIUrl":"10.1140/epjqt/s40507-025-00372-z","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":547,"journal":{"name":"EPJ Quantum Technology","volume":"12 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://epjquantumtechnology.springeropen.com/counter/pdf/10.1140/epjqt/s40507-025-00372-z","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145144289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards satellite tests combining general relativity and quantum mechanics through quantum optical interferometry: progress on the deep space quantum link","authors":"Makan Mohageg,&nbsp;Charis Anastopoulos,&nbsp;Olivia Brasher,&nbsp;Jason Gallicchio,&nbsp;Bei Lok Hu,&nbsp;Thomas Jennewein,&nbsp;Spencer Johnson,&nbsp;Shih-Yuin Lin,&nbsp;Alexander Ling,&nbsp;Alexander Lohrmann,&nbsp;Christoph Marquardt,&nbsp;Luca Mazzarella,&nbsp;Matthias Meister,&nbsp;Raymond Newell,&nbsp;Albert Roura,&nbsp;Giuseppe Vallone,&nbsp;Paolo Villoresi,&nbsp;Lisa Wörner,&nbsp;Paul Kwiat","doi":"10.1140/epjqt/s40507-025-00370-1","DOIUrl":"10.1140/epjqt/s40507-025-00370-1","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":547,"journal":{"name":"EPJ Quantum Technology","volume":"12 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://epjquantumtechnology.springeropen.com/counter/pdf/10.1140/epjqt/s40507-025-00370-1","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145144333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unified hybrid quantum classical neural network framework for detecting distributed denial of service and Android mobile malware attacks","authors":"Sridevi S,&nbsp;Indira B,&nbsp;Geetha S,&nbsp;Balachandran S,&nbsp;Gorkem Kar,&nbsp;Shangirne Kharbanda","doi":"10.1140/epjqt/s40507-025-00380-z","DOIUrl":"10.1140/epjqt/s40507-025-00380-z","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":547,"journal":{"name":"EPJ Quantum Technology","volume":"12 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://epjquantumtechnology.springeropen.com/counter/pdf/10.1140/epjqt/s40507-025-00380-z","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145167502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transformer-based quantum error decoding enhanced by QGANs: towards scalable surface code correction algorithms","authors":"Cewen Tian,&nbsp;Zaixu Fan,&nbsp;Xiaoxuan Guo,&nbsp;Xinying Song,&nbsp;Yanbing Tian","doi":"10.1140/epjqt/s40507-025-00383-w","DOIUrl":"10.1140/epjqt/s40507-025-00383-w","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":547,"journal":{"name":"EPJ Quantum Technology","volume":"12 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://epjquantumtechnology.springeropen.com/counter/pdf/10.1140/epjqt/s40507-025-00383-w","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145144223","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantum powered credit risk assessment: a novel approach using Hybrid Quantum-Classical Deep Neural Network for Row-Type Dependent Predictive Analysis","authors":"Minati Rath,&nbsp;Hema Date","doi":"10.1140/epjqt/s40507-025-00323-8","DOIUrl":"10.1140/epjqt/s40507-025-00323-8","url":null,"abstract":"<div><p>The integration of Quantum Deep Learning (QDL) techniques into the landscape of financial risk analysis presents a promising avenue for innovation. This study introduces a framework for credit risk assessment in the banking sector, combining quantum deep learning techniques with adaptive modeling for Row-Type Dependent Predictive Analysis (RTDPA). By leveraging RTDPA, the proposed approach tailors predictive models to different loan categories, aiming to enhance the accuracy and efficiency of credit risk evaluation. While this work explores the potential of integrating quantum methods with classical deep learning for risk assessment, it focuses on the feasibility and performance of this hybrid framework rather than claiming transformative industry-wide impacts. The findings offer insights into how quantum techniques can complement traditional financial analysis, paving the way for further advancements in predictive modeling for credit risk.</p></div>","PeriodicalId":547,"journal":{"name":"EPJ Quantum Technology","volume":"12 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://epjquantumtechnology.springeropen.com/counter/pdf/10.1140/epjqt/s40507-025-00323-8","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145144039","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Performance comparison of the quantum and classical deep Q-learning approaches in dynamic environments control","authors":"Aramchehr Zare,&nbsp;Mehrdad Boroushaki","doi":"10.1140/epjqt/s40507-025-00381-y","DOIUrl":"10.1140/epjqt/s40507-025-00381-y","url":null,"abstract":"<div><p>There is a lack of adequate studies on dynamic environments control for Quantum Reinforcement Learning (QRL) algorithms, representing a significant gap in this field. This study contributes to bridging this gap by demonstrating the potential of quantum RL algorithms to effectively handle dynamic environments. In this research, the performance and robustness of Quantum Deep Q-learning Networks (DQN) were examined in two dynamic environments, Cart Pole and Lunar Lander, by using three distinct quantum Ansatz layers: RealAmplitudes, EfficientSU2, and TwoLocal. The quantum DQNs were compared with classical DQN algorithms in terms of convergence speed, loss minimization, and Q-value behavior. It was observed that the RealAmplitudes Ansatz outperformed the other quantum circuits, demonstrating faster convergence and superior performance in minimizing the loss function. To assess robustness, the pole length was increased in the Cart Pole environment, and a wind function was added to the Lunar Lander environment after the 50th episode. All three quantum Ansatz layers were found to maintain robust performance under disturbed conditions, with consistent reward values, loss minimization, and stable Q-value distributions. Although the proposed QRL demonstrates competitive results overall, classical RL can surpass them in convergence speed under specific conditions.</p></div>","PeriodicalId":547,"journal":{"name":"EPJ Quantum Technology","volume":"12 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://epjquantumtechnology.springeropen.com/counter/pdf/10.1140/epjqt/s40507-025-00381-y","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145143625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The temporal resolution limit in quantum sensing","authors":"Cong-Gang Song,&nbsp;Qing-yu Cai","doi":"10.1140/epjqt/s40507-025-00377-8","DOIUrl":"10.1140/epjqt/s40507-025-00377-8","url":null,"abstract":"<div><p>Temporal resolution is a critical figure of merit in quantum sensing. This study combines the distinguishable condition of quantum states with quantum speed limits to establish a lower bound on interrogation time. When the interrogation time falls below this bound, the output state becomes statistically indistinguishable from the input state, and the information will inevitably be lost in noise. Without loss of generality, we extend these conclusions to time-dependent signal Hamiltonian. In theory, leveraging certain quantum control techniques allows us to calculate the minimum interrogation time for arbitrary signal Hamiltonian. Finally, we illustrate the impact of quantum speed limits on magnetic field measurements and temporal resolution.</p></div>","PeriodicalId":547,"journal":{"name":"EPJ Quantum Technology","volume":"12 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://epjquantumtechnology.springeropen.com/counter/pdf/10.1140/epjqt/s40507-025-00377-8","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145143547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intrinsic quality factors approaching 10 million in superconducting planar resonators enabled by spiral geometry","authors":"Yusuke Tominaga,&nbsp;Shotaro Shirai,&nbsp;Yuji Hishida,&nbsp;Hirotaka Terai,&nbsp;Atsushi Noguchi","doi":"10.1140/epjqt/s40507-025-00367-w","DOIUrl":"10.1140/epjqt/s40507-025-00367-w","url":null,"abstract":"<div><p>This study investigates the use of spiral geometry in superconducting resonators to achieve high intrinsic quality factors, crucial for applications in quantum computation and quantum sensing. We fabricated Archimedean Spiral Resonators (ASRs) using domain-matched epitaxially grown titanium nitride (TiN) on silicon wafers, achieving intrinsic quality factors of <span>(Q_{mathrm{i}} = (9.6 pm 1.5) times 10^{6})</span> at the single-photon level and <span>(Q_{mathrm{i}} = (9.91 pm 0.39) times 10^{7})</span> at high power, which is more than twice as high as those for coplanar waveguide (CPW) resonators under identical conditions on the same chip. We conducted a comprehensive numerical analysis using COMSOL to calculate surface participation ratios (PRs) at critical interfaces: metal-air, metal-substrate, and substrate-air. Our findings reveal that ASRs have lower PRs than CPWs, explaining their superior quality factors and reduced coupling to two-level systems (TLSs).</p></div>","PeriodicalId":547,"journal":{"name":"EPJ Quantum Technology","volume":"12 1","pages":""},"PeriodicalIF":5.6,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://epjquantumtechnology.springeropen.com/counter/pdf/10.1140/epjqt/s40507-025-00367-w","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145143312","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}