In this article, we consider second-generation (2G) quantum repeaters (QRs) for creating long-distance entanglement in quantum networks. Combining a distance-dependent depolarizing error model with the nonlocal Bell state purification procedure required by 2G QRs leads to an error model consisting of correlated and biased errors. To correct correlated errors, nonsymmetric Calderbank–Steane–Shor (CSS) codes with joint decoding between stations can be used. The dominating errors are biased, such that different repeater stations suffer from different types of errors. To mitigate this, different quantum codes can be used at the stations, optimized for the specific error model of the station. To comply with the 2G QR procedure, the codes used in neighboring stations must allow for the transversal implementation of nonlocal logical cnot gates across the two stations or, alternatively, nonlocal cz gates combined with logical Hadamard gates. We provide a complete characterization of pairs of CSS codes that allow cnot or cz transversality, and examine an explicit family of mirrored CSS codes allowing cz transversality. We verify Hadamard gate transversality using our framework and show the importance of the logical qubit mapping matrix. Also, we conclude that using different QECCs does not lead to universal computation with the Clifford + $T$ gate set. Finally, we study the entanglement generation rate (EGR) in 2G QRs with limited quantum memory, minimizing the number of intermediate stations for a given fidelity and EGR. By simulation, we observe that nonsymmetric and mirrored structure QECCs outperform the conventional approach of using symmetric CSS codes at the repeater stations.
{"title":"Quantum Error Correction for Second-Generation Quantum Repeaters","authors":"Dawei Jiao;Mahdi Bayanifar;Alexei Ashikhmin;Olav Tirkkonen","doi":"10.1109/TQE.2025.3649561","DOIUrl":"https://doi.org/10.1109/TQE.2025.3649561","url":null,"abstract":"In this article, we consider second-generation (2G) quantum repeaters (QRs) for creating long-distance entanglement in quantum networks. Combining a distance-dependent depolarizing error model with the nonlocal Bell state purification procedure required by 2G QRs leads to an error model consisting of correlated and biased errors. To correct correlated errors, nonsymmetric Calderbank–Steane–Shor (CSS) codes with joint decoding between stations can be used. The dominating errors are biased, such that different repeater stations suffer from different types of errors. To mitigate this, different quantum codes can be used at the stations, optimized for the specific error model of the station. To comply with the 2G QR procedure, the codes used in neighboring stations must allow for the transversal implementation of nonlocal logical <sc>cnot</small> gates across the two stations or, alternatively, nonlocal <sc>cz</small> gates combined with logical Hadamard gates. We provide a complete characterization of pairs of CSS codes that allow <sc>cnot</small> or <sc>cz</small> transversality, and examine an explicit family of mirrored CSS codes allowing <sc>cz</small> transversality. We verify Hadamard gate transversality using our framework and show the importance of the logical qubit mapping matrix. Also, we conclude that using different QECCs does not lead to universal computation with the Clifford + <inline-formula><tex-math>$T$</tex-math></inline-formula> gate set. Finally, we study the entanglement generation rate (EGR) in 2G QRs with limited quantum memory, minimizing the number of intermediate stations for a given fidelity and EGR. By simulation, we observe that nonsymmetric and mirrored structure QECCs outperform the conventional approach of using symmetric CSS codes at the repeater stations.","PeriodicalId":100644,"journal":{"name":"IEEE Transactions on Quantum Engineering","volume":"7 ","pages":"1-17"},"PeriodicalIF":4.6,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11320840","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147361163","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}
Pub Date : 2025-12-31DOI: 10.1109/TQE.2025.3649709
Marco Passafiume;Raviraj Adve;Boniface Yogendran;Bhashyam Balaji
Nonclassical radar and lidar systems have received substantial interest recently; however, although many experimental demonstrations have provided deep physical knowledge of such systems, there remains a lack of effective system models to obtain fundamental metrics such as range resolution as a function of system parameters. This work introduces a high-fidelity simulation platform to mimic a certain type of quantum radar, specifically a recently proposed one based on temporal coincidences that arise due to entanglement. Specifically, the system measures coincidences between events related to a reference source and those related to the backscattering of photons from targets. The large number of events—and their complex interaction with system components—makes a realistic simulation challenging. As an initial assessment, in this article, we develop a simulator to estimate the expected point spread function (PSF), and thereby the range resolution, considering various coincidence window time widths and system nonidealities. The estimate is based on the numerical computation of the correlation between the reference traces shifted along the time domain and traces of backscattered photons (along with noise photons). The simulated results are comparable to available experimental results, illustrating the fidelity of the simulation engine. A crucial result is that, unlike a classical radar, the PSF and range resolution depend upon the environmental noise and multiple system parameters, not just the transmitted waveform.
{"title":"A Sparse-Event Simulation Engine to Model Coincidence-Based Ranging Architectures in Quantum Lidar","authors":"Marco Passafiume;Raviraj Adve;Boniface Yogendran;Bhashyam Balaji","doi":"10.1109/TQE.2025.3649709","DOIUrl":"https://doi.org/10.1109/TQE.2025.3649709","url":null,"abstract":"Nonclassical radar and lidar systems have received substantial interest recently; however, although many experimental demonstrations have provided deep physical knowledge of such systems, there remains a lack of effective system models to obtain fundamental metrics such as range resolution as a function of system parameters. This work introduces a high-fidelity simulation platform to mimic a certain type of quantum radar, specifically a recently proposed one based on temporal coincidences that arise due to entanglement. Specifically, the system measures coincidences between events related to a reference source and those related to the backscattering of photons from targets. The large number of events—and their complex interaction with system components—makes a realistic simulation challenging. As an initial assessment, in this article, we develop a simulator to estimate the expected point spread function (PSF), and thereby the range resolution, considering various coincidence window time widths and system nonidealities. The estimate is based on the numerical computation of the correlation between the reference traces shifted along the time domain and traces of backscattered photons (along with noise photons). The simulated results are comparable to available experimental results, illustrating the fidelity of the simulation engine. A crucial result is that, unlike a classical radar, the PSF and range resolution depend upon the environmental noise and multiple system parameters, not just the transmitted waveform.","PeriodicalId":100644,"journal":{"name":"IEEE Transactions on Quantum Engineering","volume":"7 ","pages":"1-21"},"PeriodicalIF":4.6,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11320830","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026450","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}
The manufacturing industry encounters numerous optimization problems, one of which is the optimization of storage location assignment (OSLA) problem in logistics. OSLA is a combinatorial optimization problem focused on improving the efficiency of picking operations in logistics centers. We explore quantum annealing (QA) as a potential solution to combinatorial optimization problems and investigate its applicability to the OSLA. The objective function for this optimization is the average travel distance of workers to their assigned destinations. However, this value is derived by solving the traveling salesman problem for multiple orders, which is itself a combinatorial optimization problem. Therefore, it cannot be analytically represented in a quadratic unconstrained binary optimization form. To address this limitation, we employed black-box optimization with annealing, which combines a surrogate model with an annealing algorithm, an approach that has recently gained attention in applied research involving QA. To evaluate the effectiveness of quantum computing, we compared results obtained using simulated annealing (SA) with those obtained using QA. In addition, to assess the optimization performance of our proposed method, we compared it with a genetic algorithm (GA) that did not utilize a surrogate model of the objective function. QA demonstrated a higher probability of finding the optimal solution (33.3% versus 26.7% with SA). However, the optimization performance of the GA surpassed that of the proposed method. Our analysis suggests that the relatively lower performance of our method was primarily attributable to the strong influence of constraints. The optimization performance can be improved by incorporating methods that consider the uncertainty of surrogate model predictions, such as the lower confidence bound.
{"title":"Black-Box Optimization of the Storage Location Assignment Problem in Logistics Centers Using an Annealing Algorithm","authors":"Hiromitsu Kigure;Takeshi Baba;Makoto Taniguchi;Hirotaka Kaji","doi":"10.1109/TQE.2025.3646010","DOIUrl":"https://doi.org/10.1109/TQE.2025.3646010","url":null,"abstract":"The manufacturing industry encounters numerous optimization problems, one of which is the optimization of storage location assignment (OSLA) problem in logistics. OSLA is a combinatorial optimization problem focused on improving the efficiency of picking operations in logistics centers. We explore quantum annealing (QA) as a potential solution to combinatorial optimization problems and investigate its applicability to the OSLA. The objective function for this optimization is the average travel distance of workers to their assigned destinations. However, this value is derived by solving the traveling salesman problem for multiple orders, which is itself a combinatorial optimization problem. Therefore, it cannot be analytically represented in a quadratic unconstrained binary optimization form. To address this limitation, we employed black-box optimization with annealing, which combines a surrogate model with an annealing algorithm, an approach that has recently gained attention in applied research involving QA. To evaluate the effectiveness of quantum computing, we compared results obtained using simulated annealing (SA) with those obtained using QA. In addition, to assess the optimization performance of our proposed method, we compared it with a genetic algorithm (GA) that did not utilize a surrogate model of the objective function. QA demonstrated a higher probability of finding the optimal solution (33.3% versus 26.7% with SA). However, the optimization performance of the GA surpassed that of the proposed method. Our analysis suggests that the relatively lower performance of our method was primarily attributable to the strong influence of constraints. The optimization performance can be improved by incorporating methods that consider the uncertainty of surrogate model predictions, such as the lower confidence bound.","PeriodicalId":100644,"journal":{"name":"IEEE Transactions on Quantum Engineering","volume":"7 ","pages":"1-12"},"PeriodicalIF":4.6,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11304740","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145982299","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}
Pub Date : 2025-12-18DOI: 10.1109/TQE.2025.3645732
Shin-Yi Wen;Bor-Sen Chen;Chun-Liang Lin
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 $H_{infty }$ 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 $H_{infty }$ 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 $H_{infty }$ UTOBRQTTC design strategy. Furthermore, the nonlinear Hamilton–Jacobi inequality-constrained optimization problem of the optimal robust $H_{infty }$ 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.
{"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}
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.
{"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}
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.
{"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}
Pub Date : 2025-12-09DOI: 10.1109/TQE.2025.3642110
Purin Pongpanich;Tanasanee Phienthrakul
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.
{"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}
Pub Date : 2025-12-08DOI: 10.1109/TQE.2025.3641027
Kai Zhang;Sen Kuang
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.
{"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}
Pub Date : 2025-12-04DOI: 10.1109/TQE.2025.3640361
Sana Javed;Sergio Colet;Francisco Garcia-Herrero;Óscar Ruano;Juan Antonio Maestro;Bane Vasić;Mark F. Flanagan
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.
{"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}
Pub Date : 2025-12-01DOI: 10.1109/TQE.2025.3638878
Subhadeep Mondal;Amit Kumar Dutta
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.
{"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}
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