IEEE Transactions on Quantum Engineering最新文献

{"title":"Robust $H_{infty }$ Uncertainties-Tolerant Observer-Based Reference Quantum Trajectory Tracking Control for Lindblad Master Equation","authors":"Shin-Yi Wen;Bor-Sen Chen;Chun-Liang Lin","doi":"10.1109/TQE.2025.3645732","DOIUrl":"https://doi.org/10.1109/TQE.2025.3645732","url":null,"abstract":"In this article, a robust output feedback reference quantum trajectory tracking control design is proposed through the simultaneous continuous weak measurement of noncommuting observables. Using the robust <inline-formula><tex-math>$H_{infty }$</tex-math></inline-formula> uncertainties-tolerant observer-based reference quantum trajectory tracking control (UTOBRQTTC) design strategy, the proposed method can robustly estimate the quantum trajectory and robustly track a sequence of any reference quantum states against undesired uncertainties and potential unavailable fault signals. Smoothed signal models are embedded into the augmented bilinear quantum system derived from the Lindblad master equation. With the regression of unavailable system and sensor fault signals by smoothed models, the proposed <inline-formula><tex-math>$H_{infty }$</tex-math></inline-formula> UTOBRQTTC design of the augmented bilinear quantum system can proactively compensate for the corruption of fault signals. Therefore, robust quantum trajectory estimation and reference quantum trajectory tracking can be achieved simultaneously via the proposed robust <inline-formula><tex-math>$H_{infty }$</tex-math></inline-formula> UTOBRQTTC design strategy. Furthermore, the nonlinear Hamilton–Jacobi inequality-constrained optimization problem of the optimal robust <inline-formula><tex-math>$H_{infty }$</tex-math></inline-formula> UTOBRQTTC design strategy can be treated as a linear matrix inequality (LMI)-constrained optimization problem by the upper bound of spectral radius of the augmented bilinear quantum system and the proposed two-step procedure, which can be efficiently solved with the help of the MATLAB LMI Toolbox. Finally, several simulation examples of two-level bilinear quantum systems represented by the Lindblad master equation are provided to demonstrate the estimation performance of quantum trajectory and fault signals and any arbitrary signal tracking performance for more practical applications of bilinear quantum systems.","PeriodicalId":100644,"journal":{"name":"IEEE Transactions on Quantum Engineering","volume":"7 ","pages":"1-25"},"PeriodicalIF":4.6,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11304164","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145982317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrated Encoding and Quantization to Enhance Quanvolutional Neural Networks","authors":"Daniele Lizzio Bosco;Beatrice Portelli;Giuseppe Serra","doi":"10.1109/TQE.2025.3646040","DOIUrl":"https://doi.org/10.1109/TQE.2025.3646040","url":null,"abstract":"Image processing is one of the most promising applications for quantum machine learning. Quanvolutional neural networks with nontrainable parameters are the preferred solution to run on current and near future quantum devices. The typical input preprocessing pipeline for quanvolutional layers comprises of four steps: optional input binary quantization, encoding classical data into quantum states, processing the data to obtain the final quantum states, and decoding quantum states back to classical outputs. In this article, we propose two ways to enhance the efficiency of quanvolutional models. First, we propose a flexible data quantization approach with memoization, applicable to any encoding method. This allows us to increase the number of quantization levels to retain more information or lower them to reduce the amount of circuit executions. Second, we introduce a new integrated encoding strategy, which combines the encoding and processing steps in a single circuit. This method allows great flexibility on several architectural parameters (e.g., number of qubits, filter size, and circuit depth) making them adjustable to quantum hardware requirements. We compare our proposed integrated model with a classical convolutional neural network and the well-known rotational encoding method, on two different classification tasks. The results demonstrate that our proposed model encoding exhibits a comparable or superior performance to the other models while requiring fewer quantum resources.","PeriodicalId":100644,"journal":{"name":"IEEE Transactions on Quantum Engineering","volume":"7 ","pages":"1-19"},"PeriodicalIF":4.6,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11303607","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On-Demand Resource Allocation for a Quantum Network Hub","authors":"Scarlett Gauthier;Thirupathaiah Vasantam;Gayane Vardoyan","doi":"10.1109/TQE.2025.3641834","DOIUrl":"https://doi.org/10.1109/TQE.2025.3641834","url":null,"abstract":"To effectively support the execution of quantum network applications for multiple sets of user-controlled quantum nodes, a quantum network must efficiently allocate shared resources. We study traffic models for a type of quantum network hub called an entanglement generation switch (EGS), a device that allocates resources to enable entanglement generation between nodes in response to user-generated demand. We propose an on-demand resource allocation algorithm, where a demand is either blocked if no resources are available or else results in immediate resource allocation. We model the EGS as an Erlang loss system, with demands corresponding to sessions whose arrival is modeled as a Poisson process. To reflect the operation of a practical quantum switch, our model captures scenarios where a resource is allocated for batches of entanglement generation attempts, possibly interleaved with calibration periods for the quantum network nodes. Calibration periods are necessary to correct against drifts or jumps in the physical parameters of a quantum node that occur on a timescale that is long compared to the duration of an attempt. We then derive a formula for the demand blocking probability under three different traffic scenarios using analytical methods from applied probability and queueing theory. We prove an insensitivity theorem which guarantees that the probability a demand is blocked only depends upon the mean duration of each entanglement generation attempt and calibration period, and is not sensitive to the underlying distributions of attempt and calibration period duration. We provide numerical results to support our analysis. Our numerical results suggest that there exist parameter regimes where it is beneficial for nodes to relinquish control of EGS resources during their calibration periods. This benefit is quantified by the blocking probability and the total entanglement generated in a fixed period of time. Our work is the first analysis of traffic characteristics at an EGS system and provides a valuable analytic tool for devising performance driven resource allocation algorithms.","PeriodicalId":100644,"journal":{"name":"IEEE Transactions on Quantum Engineering","volume":"7 ","pages":"1-30"},"PeriodicalIF":4.6,"publicationDate":"2025-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11288080","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dual-Discriminator Hybrid Quantum Generative Adversarial Networks for Improved GAN Performance","authors":"Purin Pongpanich;Tanasanee Phienthrakul","doi":"10.1109/TQE.2025.3642110","DOIUrl":"https://doi.org/10.1109/TQE.2025.3642110","url":null,"abstract":"This study presents an investigation of the dual-discriminator hybrid quantum generative adversarial network (DDHQ-GAN), a framework designed to enhance the performance of conventional generative adversarial networks (GANs) through the incorporation of a hybrid quantum discriminator. The proposed DDHQ-GAN architecture comprises three primary components: a generator and two discriminators. The research evaluates the efficacy of the DDHQ-GAN in comparison with existing GAN variants, employing the Fréchet inception distance (FID) as a quantitative metric to assess image generation quality. The study further examines the interplay between the structural configurations of parameterized quantum circuits, classical neural network architectures, and model hyperparameters, using the Modified National Institute of Standards and Technology (MNIST) dataset as the experimental benchmark. Empirical results demonstrate that the DDHQ-GAN achieves superior performance, reflected by lower FID scores, while incurring only a marginal increase in the number of parameters and quantum computational resources.","PeriodicalId":100644,"journal":{"name":"IEEE Transactions on Quantum Engineering","volume":"7 ","pages":"1-14"},"PeriodicalIF":4.6,"publicationDate":"2025-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11288093","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145982290","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimal Control-Assisted Rapid Quantum State Transfer on 1-D Spin Chain","authors":"Kai Zhang;Sen Kuang","doi":"10.1109/TQE.2025.3641027","DOIUrl":"https://doi.org/10.1109/TQE.2025.3641027","url":null,"abstract":"Traditional many-body teleportation relies on the strong interaction property of a quantum many-body system, which usually requires numerous qubits and entanglement resources, making it difficult to realize experimentally. A natural scheme is to use a 1-D spin chain with simple structure to realize many-body teleportation. In this article, we analyze the conditions for general quantum many-body teleportation and construct an effective control Hamiltonian, realizing quantum many-body teleportation on the controlled 1-D spin chain. Our scheme, which only requires forward evolution and local measurements, can be used to perform quantum state transfer without the special presetting and modulation of coupling parameters of the chain and without strict control over the evolution time, thereby enhancing the experimental realizability. Furthermore, we improve the efficiency and accuracy of quantum state transfer by introducing quantum optimal control technique to optimize the control pulse sequences.","PeriodicalId":100644,"journal":{"name":"IEEE Transactions on Quantum Engineering","volume":"7 ","pages":"1-14"},"PeriodicalIF":4.6,"publicationDate":"2025-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11283091","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145886656","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Low-Complexity Syndrome-Based Linear Programming Decoding of Quantum LDPC Codes","authors":"Sana Javed;Sergio Colet;Francisco Garcia-Herrero;Óscar Ruano;Juan Antonio Maestro;Bane Vasić;Mark F. Flanagan","doi":"10.1109/TQE.2025.3640361","DOIUrl":"https://doi.org/10.1109/TQE.2025.3640361","url":null,"abstract":"This article proposes a novel low-complexity syndrome-based linear programming (SB-LP) decoding algorithm for decoding quantum low-density parity-check codes. Under the code-capacity model, the SB-LP decoder can be used as a standalone decoder; however, it is particularly powerful when used as a postprocessing step following SB min-sum (SB-MS) decoding. In the latter case, the proposed decoder is shown to be capable of significantly reducing the error floor of the SB-MS decoder for both flooded and layered SB-MS scheduling. Also, an early stopping criterion is introduced to decide when to activate the SB-LP algorithm, avoiding executing a predefined maximum number of iterations for the SB-MS decoder. Simulation results show, for some example hypergraph and generalized bicycle (GB) codes, that the proposed decoder can lower the error floor by one to three orders of magnitude compared to SB-MS for the same total number of decoding iterations. Furthermore, for the class of GB codes, it is shown that as the minimum distance of the code increases, the logical error rate provided by the proposed decoder also improves, indicating that the solution is scalable. Under the circuit-level noise model, it is shown that while the SB-LP decoder does not fully replace the need for ordered statistics decoding (OSD) when flooded SB-MS is used as a preliminary step, it reduces the number of calls to the OSD postprocessor, which directly impacts the overall latency. In addition, the method offers a syndrome-matching decoder and improves the accuracy of the logical error rate for bivariate bicycle codes of distances 6 to 18, particularly at low error rates, when compared to the belief propagation+OSD benchmark.","PeriodicalId":100644,"journal":{"name":"IEEE Transactions on Quantum Engineering","volume":"7 ","pages":"1-19"},"PeriodicalIF":4.6,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11278092","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145982312","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Relative Entropy-Based Training of Quantum Neural Networks","authors":"Subhadeep Mondal;Amit Kumar Dutta","doi":"10.1109/TQE.2025.3638878","DOIUrl":"https://doi.org/10.1109/TQE.2025.3638878","url":null,"abstract":"Quantum neural networks (QNNs) are gaining attention as versatile models for quantum machine learning, but training them effectively remains a challenge. Most existing approaches, such as quantum multilayer perceptrons, use fidelity-based cost functions. While well-suited for pure states, these measures are less reliable when inputs and outputs are mixed states—a situation common in learning quantum channels. In this work, we introduce a training framework built on a relative entropy-inspired cost function. By quantifying the directional divergence between learned and target states, relative entropy provides a more informative and principled measure than linear fidelity, naturally capturing both spectral and eigenvector differences in mixed states. This approach preserves the completely positive structure of the network, supports efficient backpropagation in layered QNN configurations, and achieves improved accuracy and convergence over fidelity-based training. These results highlight entropy-based optimization as a promising path toward scalable, robust, and noise-resilient quantum learning.","PeriodicalId":100644,"journal":{"name":"IEEE Transactions on Quantum Engineering","volume":"7 ","pages":"1-14"},"PeriodicalIF":4.6,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11271543","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145886633","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Feynman Meets Turing: Computability Aspects of Exact Circuit Synthesis, Gate Efficiency, and the Spectral Gap Conjecture","authors":"Yannik N. Boeck;Holger Boche;Frank H.P. Fitzek","doi":"10.1109/TQE.2025.3636049","DOIUrl":"https://doi.org/10.1109/TQE.2025.3636049","url":null,"abstract":"We consider exact quantum circuit synthesis, quantum gate efficiency, and the spectral gap conjecture from the perspective of computable analysis. Circuit synthesis, in both its exact and its approximate variant, is fundamental to the circuit model of quantum computing. As an engineering problem, however, the practical and theoretical aspects of quantum circuit synthesis are far from being fully understood. Particularly, this concerns explicit methods for gate-agnostic circuit synthesis and questions of gate efficiency. More than 20 years ago, Harrow et al. published their famous spectral gap theorem: given a suitable family of quantum gates, it is possible to approximate any unitary transformation by means of a quantum circuit whose length is proportional to the required accuracy’s logarithm. Moreover, Harrow et al. suspected that all universal gate families allow for this type of approximation, a hypothesis that became known as the spectral gap conjecture and remains unproven until today. Being an entirely classical task, quantum circuit synthesis must be considered in the context of digital computing, that is, in the context of Turing computability and computable analysis. Using the relevant mathematical framework, we establish no-go results concerning exact quantum circuit synthesis and quantum big-O analysis. Our findings relate to the theory of approximate t-designs, which has recently received notable attention through the literature. Moreover, as follows from our findings, the existence of an algorithm that computes leading big-O coefficients would prove the spectral gap conjecture true within the computable special unitary group.","PeriodicalId":100644,"journal":{"name":"IEEE Transactions on Quantum Engineering","volume":"7 ","pages":"1-31"},"PeriodicalIF":4.6,"publicationDate":"2025-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11266935","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145982306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transmon Qubit Modeling and Characterization for Dark Matter Search","authors":"Roberto Moretti;Danilo Labranca;Pietro Campana;Rodolfo Carobene;Marco Gobbo;Manuel A. Castellanos-Beltran;David Olaya;Peter F. Hopkins;Leonardo Banchi;Matteo Borghesi;Alessandro Candido;Stefano Carrazza;Hervè Atsè Corti;Alessandro D’Elia;Marco Faverzani;Elena Ferri;Angelo Nucciotti;Luca Origo;Andrea Pasquale;Alex Stephane Piedjou Komnang;Alessio Rettaroli;Simone Tocci;Claudio Gatti;Andrea Giachero","doi":"10.1109/TQE.2025.3633176","DOIUrl":"https://doi.org/10.1109/TQE.2025.3633176","url":null,"abstract":"This study presents the design, simulation, and experimental characterization of a superconducting transmon qubit circuit prototype for potential applications in dark matter detection experiments. We describe a planar circuit design featuring two noninteracting transmon qubits, one with fixed frequency and the other flux tunable. Finite-element simulations were employed to extract key Hamiltonian parameters and optimize component geometries. The qubit was fabricated and then characterized at 20 mK, allowing for a comparison between simulated and measured qubit parameters. Good agreement was found for transition frequencies and anharmonicities (within 1% and 10%, respectively) while coupling strengths exhibited larger discrepancies (30%). We discuss potential causes for measured coherence times falling below expectations (<inline-formula><tex-math>$T_{1}sim ,$</tex-math></inline-formula>1–2 <italic>μ</i>s) and propose strategies for future design improvements. Notably, we demonstrate the application of a hybrid 3D–2D simulation approach for energy participation ratio evaluation, yielding a more accurate estimation of dielectric losses. This work represents an important first step in developing planar quantum nondemolition single-photon counters for dark matter searches, particularly for axion and dark photon detection schemes.","PeriodicalId":100644,"journal":{"name":"IEEE Transactions on Quantum Engineering","volume":"7 ","pages":"1-8"},"PeriodicalIF":4.6,"publicationDate":"2025-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11249713","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145729280","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hardware-Aware and Resource-Efficient Circuit Packing and Scheduling on Trapped-Ion Quantum Computers","authors":"Miguel Palma;Shuwen Kan;Wenqi Wei;Juntao Chen;Kaixun Hua;Sara Mouradian;Ying Mao","doi":"10.1109/TQE.2025.3632540","DOIUrl":"https://doi.org/10.1109/TQE.2025.3632540","url":null,"abstract":"The rapid expansion of quantum cloud services has led to long job queues due to single-tenant execution models that underutilize hardware resources. Quantum multiprogramming (QMP) mitigates this by executing multiple circuits in parallel on a single device, but existing methods target superconducting systems with limited connectivity, high crosstalk, and lower gate fidelity. Trapped-ion architecture, with all-to-all connectivity, long coherence times, and high-fidelity mid-circuit measurement properties, presents itself as a more suitable platform for scalable QMP. We present CircPack, a hardware-aware circuit packing framework designed for modular trapped-ion devices based on the quantum charge-coupled device (QCCD) architecture. CircPack formulates static circuit scheduling as a 2-D packing problem with hardware-specific shuttling constraints. Compared to superconducting-based QMP approaches, CircPack achieves up to 70.72% better fidelity, 62.67% higher utilization, and 32.80% improved layer reduction. This framework is also capable of scalable balanced scheduling across a cluster of independent QCCD modules, highlighting trapped-ion systems’ potential in improving the throughput of quantum cloud computing in the near future.","PeriodicalId":100644,"journal":{"name":"IEEE Transactions on Quantum Engineering","volume":"7 ","pages":"1-15"},"PeriodicalIF":4.6,"publicationDate":"2025-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11249463","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145778454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}