IEEE Transactions on Quantum Engineering最新文献

{"title":"Quantum Switches for Gottesman–Kitaev–Preskill Qubit-Based All-Photonic Quantum Networks","authors":"Mohadeseh Azari;Paul Polakos;Kaushik P. Seshadreesan","doi":"10.1109/TQE.2024.3476009","DOIUrl":"https://doi.org/10.1109/TQE.2024.3476009","url":null,"abstract":"The Gottesman–Kitaev–Preskill (GKP) code, being information theoretically near optimal for quantum communication over Gaussian thermal-loss optical channels, is likely to be the encoding of choice for advanced quantum networks of the future. Quantum repeaters based on GKP-encoded light have been shown to support high end-to-end entanglement rates across large distances despite realistic finite squeezing in GKP code preparation and homodyne detection inefficiencies. Here, we introduce a quantum switch for GKP qubit-based quantum networks. Its architecture involves multiplexed GKP qubit-based entanglement link generation with clients and their all-photonic storage, enabled by GKP qubit graph state resources. The switch uses a multiclient generalization of a recently introduced entanglement-ranking-based link matching heuristic for bipartite entanglement distribution between clients via entanglement swapping. Since generating the GKP qubit graph state resource is hardware intensive, given a total resource budget and an arbitrary layout of clients, we address the question of their optimal allocation to the different client–pair connections served by the switch such that the switch's sum throughput is maximized while also being fair in terms of the individual entanglement rates. We illustrate our results for an exemplary data center network, where the data center is a client of a switch, and all of its other clients aim to connect to the data center alone—a scenario that also captures the general case of a gateway router connecting a local area network to a global network. Together with compatible quantum repeaters, our quantum switch provides a way to realize quantum networks of arbitrary topology.","PeriodicalId":100644,"journal":{"name":"IEEE Transactions on Quantum Engineering","volume":"5 ","pages":"1-15"},"PeriodicalIF":0.0,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10720623","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142672018","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":"HyQ2: A Hybrid Quantum Neural Network for NextG Vulnerability Detection","authors":"Yifeng Peng;Xinyi Li;Zhiding Liang;Ying Wang","doi":"10.1109/TQE.2024.3481280","DOIUrl":"https://doi.org/10.1109/TQE.2024.3481280","url":null,"abstract":"As fifth-generation (5G) and next-generation communication systems advance and find widespread application in critical infrastructures, the importance of vulnerability detection becomes increasingly critical. The growing complexity of these systems necessitates rigorous testing and analysis, with stringent requirements for both accuracy and speed. In this article, we present a state-of-the-art supervised hybrid quantum neural network named HyQ2 for vulnerability detection in next-generation wireless communication systems. The proposed HyQ2 is integrated with graph-embedded and quantum variational circuits to validate and detect vulnerabilities from the 5G system's state transitions based on graphs extracted from log files. We address the limitations of classical machine learning models in processing the intrinsic linkage relationships of high-dimensional data. These models often suffer from dead neurons and excessively large outputs caused by the unbounded range of the rectified linear unit (ReLU) activation function. We propose the HyQ2 method to overcome these challenges, which constructs quantum neurons by selecting random neurons' outputs from a classical neural network. These quantum neurons are then utilized to capture more complex relationships, effectively limiting the ReLU output. Using only two qubits, our validation results demonstrate that HyQ2 outperforms traditional classical machine learning models in vulnerability detection. The small and compact variational circuit of HyQ2 minimizes the noise and errors in the measurement. Our results demonstrate that HyQ2 achieves a high area under the curve (AUC) value of 0.9708 and an accuracy of 95.91%. To test the model's performance in quantum noise environments, we simulate quantum noise by adding bit flipping, phase flipping, amplitude damping, and depolarizing noise. The results show that the prediction accuracy and receiver operating characteristic AUC value fluctuate around 0.2%, indicating HyQ2’s robustness in noisy quantum environments. In addition, the noise resilience and robustness of the HyQ2 algorithm were substantiated through experiments on the IBM quantum machine with only a 0.2% decrease compared to the simulation results.","PeriodicalId":100644,"journal":{"name":"IEEE Transactions on Quantum Engineering","volume":"5 ","pages":"1-19"},"PeriodicalIF":0.0,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10716796","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636455","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":"Noise-Aware Quantum Amplitude Estimation","authors":"Steven Herbert;Ifan Williams;Roland Guichard;Darren Ng","doi":"10.1109/TQE.2024.3476929","DOIUrl":"https://doi.org/10.1109/TQE.2024.3476929","url":null,"abstract":"In this article, based on some simple and reasonable assumptions, we derive a Gaussian noise model for quantum amplitude estimation. We provide results from quantum amplitude estimation run on various IBM superconducting quantum computers and on Quantinuum's H1 trapped-ion quantum computer to show that the proposed model is a good fit for real-world experimental data. We also show that the proposed Gaussian noise model can be easily composed with other noise models in order to capture effects that are not well described by Gaussian noise. We give a generalized procedure for how to embed this noise model into any quantum-phase-estimation-free quantum amplitude estimation algorithm, such that the amplitude estimation is “noise aware.” We then provide experimental results from running an implementation of noise-aware quantum amplitude estimation using data from an IBM superconducting quantum computer, demonstrating that the addition of “noise awareness” serves as an effective means of quantum error mitigation.","PeriodicalId":100644,"journal":{"name":"IEEE Transactions on Quantum Engineering","volume":"5 ","pages":"1-23"},"PeriodicalIF":0.0,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10711252","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142555072","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":"Local Binary and Multiclass SVMs Trained on a Quantum Annealer","authors":"Enrico Zardini;Amer Delilbasic;Enrico Blanzieri;Gabriele Cavallaro;Davide Pastorello","doi":"10.1109/TQE.2024.3475875","DOIUrl":"https://doi.org/10.1109/TQE.2024.3475875","url":null,"abstract":"Support vector machines (SVMs) are widely used machine learning models, with formulations for both classification and regression tasks. In the last years, with the advent of working quantum annealers, hybrid SVM models characterized by quantum training and classical execution have been introduced. These models have demonstrated comparable performance to their classical counterparts. However, they are limited in the training set size due to the restricted connectivity of the current quantum annealers. Hence, to take advantage of large datasets, a strategy is required. In the classical domain, local SVMs, namely, SVMs trained on the data samples selected by a \u0000<inline-formula><tex-math>$k$</tex-math></inline-formula>\u0000-nearest neighbors model, have already proven successful. Here, the local application of quantum-trained SVM models is proposed and empirically assessed. In particular, this approach allows overcoming the constraints on the training set size of the quantum-trained models while enhancing their performance. In practice, the fast local kernel support vector machine (FaLK-SVM) method, designed for efficient local SVMs, has been combined with quantum-trained SVM models for binary and multiclass classification. In addition, for comparison, FaLK-SVM has been interfaced for the first time with a classical single-step multiclass SVM model. Concerning the empirical evaluation, D-Wave's quantum annealers and real-world datasets taken from the remote sensing domain have been employed. The results have shown the effectiveness and scalability of the proposed approach, but also its practical applicability in a real-world large-scale scenario.","PeriodicalId":100644,"journal":{"name":"IEEE Transactions on Quantum Engineering","volume":"5 ","pages":"1-12"},"PeriodicalIF":0.0,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10706813","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142573633","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":"FPGA-Based Distributed Union-Find Decoder for Surface Codes","authors":"Namitha Liyanage;Yue Wu;Siona Tagare;Lin Zhong","doi":"10.1109/TQE.2024.3467271","DOIUrl":"https://doi.org/10.1109/TQE.2024.3467271","url":null,"abstract":"A fault-tolerant quantum computer must decode and correct errors faster than they appear to prevent exponential slowdown due to error correction. The Union-Find (UF) decoder is promising with an average time complexity slightly higher than \u0000<inline-formula><tex-math>$O(d^{3})$</tex-math></inline-formula>\u0000. We report a distributed version of the UF decoder that exploits parallel computing resources for further speedup. Using a field-programmable gate array (FPGA)-based implementation, we empirically show that this distributed UF decoder has a sublinear average time complexity with regard to \u0000<inline-formula><tex-math>$d$</tex-math></inline-formula>\u0000, given \u0000<inline-formula><tex-math>$O(d^{3})$</tex-math></inline-formula>\u0000 parallel computing resources. The decoding time per measurement round decreases as \u0000<inline-formula><tex-math>$d$</tex-math></inline-formula>\u0000 increases, the first time for a quantum error decoder. The implementation employs a scalable architecture called Helios that organizes parallel computing resources into a hybrid tree-grid structure. Using a Xilinx VCU129 FPGA, we successfully implement \u0000<inline-formula><tex-math>$d$</tex-math></inline-formula>\u0000 up to 21 with an average decoding time of 11.5 ns per measurement round under 0.1% phenomenological noise and 23.7 ns for \u0000<inline-formula><tex-math>$d=17$</tex-math></inline-formula>\u0000 under equivalent circuit-level noise. This performance is significantly faster than any existing decoder implementation. Furthermore, we show that Helios can optimize for resource efficiency by decoding \u0000<inline-formula><tex-math>$d=51$</tex-math></inline-formula>\u0000 on a Xilinx VCU129 FPGA with an average latency of 544 ns per measurement round.","PeriodicalId":100644,"journal":{"name":"IEEE Transactions on Quantum Engineering","volume":"5 ","pages":"1-18"},"PeriodicalIF":0.0,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10693533","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142516853","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":"SPARQ: Efficient Entanglement Distribution and Routing in Space–Air–Ground Quantum Networks","authors":"Mohamed Shaban;Muhammad Ismail;Walid Saad","doi":"10.1109/TQE.2024.3464572","DOIUrl":"https://doi.org/10.1109/TQE.2024.3464572","url":null,"abstract":"In this article, a space–air–ground quantum (SPARQ) network is developed as a means for providing a seamless on-demand entanglement distribution. The node mobility in SPARQ poses significant challenges to entanglement routing. Existing quantum routing algorithms focus on stationary ground nodes and utilize link distance as an optimality metric, which is unrealistic for dynamic systems, like SPARQ. Moreover, in contrast to the prior art that assumes homogeneous nodes, SPARQ encompasses heterogeneous nodes with different functionalities further complicates the entanglement distribution. To solve the entanglement routing problem, a deep reinforcement learning (RL) framework is proposed and trained using deep Q-network (DQN) on multiple graphs of SPARQ to account for the network dynamics. Subsequently, an entanglement distribution policy, third-party entanglement distribution (TPED), is proposed to establish entanglement between communication parties. A realistic quantum network simulator is designed for performance evaluation. Simulation results show that the TPED policy improves entanglement fidelity by 3% and reduces memory consumption by 50% compared with benchmark. The results also show that the proposed DQN algorithm improves the number of resolved teleportation requests by 39% compared with shortest path baseline and the entanglement fidelity by 2% compared with an RL algorithm that is based on long short-term memory. It also improved entanglement fidelity by 6% and 9% compared with state-of-the-art benchmarks. Moreover, the entanglement fidelity is improved by 15% compared with DQN trained on a snapshot of SPARQ. Additionally, SPARQ enhances the average entanglement fidelity by 23.5% compared with existing networks spanning only space and ground layers.","PeriodicalId":100644,"journal":{"name":"IEEE Transactions on Quantum Engineering","volume":"5 ","pages":"1-20"},"PeriodicalIF":0.0,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10684482","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142450981","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":"Hierarchical Quantum Architecture Search for Variational Quantum Algorithms","authors":"Tong Zhao;Bo Chen;Guanting Wu;Liang Zeng","doi":"10.1109/TQE.2024.3454640","DOIUrl":"https://doi.org/10.1109/TQE.2024.3454640","url":null,"abstract":"Designing efficient variational quantum algorithms (VQAs) is crucial for transforming the theoretical advantages of quantum algorithms into practical applications. In this context, quantum architecture search (QAS) has been introduced to automate the search and design of VQAs. However, current mainstream QAS algorithms typically perform both global and local searches simultaneously, which can result in high search space complexity and optimization challenges. In this paper, we propose a hierarchical quantum architecture search framework based on a two-stage search structure. In the first stage, global exploration of the overall quantum circuit structure is performed, while in the second stage, local optimization of quantum gate selection is carried out. We provide a numerical analysis of the theoretical advantages of the proposed framework in reducing the search space. To evaluate practical performance, we conduct experiments on quantum chemistry tasks with different algorithm combinations integrated into the framework. The results demonstrate the effectiveness of the hierarchical search structure in automating quantum circuit design.","PeriodicalId":100644,"journal":{"name":"IEEE Transactions on Quantum Engineering","volume":"5 ","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10666003","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142518178","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":"Quantum Speedup of the Dispersion and Codebook Design Problems","authors":"Kein Yukiyoshi;Taku Mikuriya;Hyeon Seok Rou;Giuseppe Thadeu Freitas de Abreu;Naoki Ishikawa","doi":"10.1109/TQE.2024.3450852","DOIUrl":"https://doi.org/10.1109/TQE.2024.3450852","url":null,"abstract":"In this article, we propose new formulations of max-sum and max-min dispersion problems that enable solutions via the Grover adaptive search (GAS) quantum algorithm, offering quadratic speedup. Dispersion problems are combinatorial optimization problems classified as NP-hard, which appear often in coding theory and wireless communications applications involving optimal codebook design. In turn, GAS is a quantum exhaustive search algorithm that can be used to implement full-fledged maximum-likelihood optimal solutions. In conventional naive formulations, however, it is typical to rely on a binary vector spaces, resulting in search space sizes prohibitive even for GAS. To circumvent this challenge, we instead formulate the search of optimal dispersion problem over Dicke states, an equal superposition of binary vectors with equal Hamming weights, which significantly reduces the search space leading to a simplification of the quantum circuit via the elimination of penalty terms. In addition, we propose a method to replace distance coefficients with their ranks, contributing to the reduction of the number of qubits. Our analysis demonstrates that as a result of the proposed techniques, a reduction in query complexity compared to the conventional GAS using the Hadamard transform is achieved, enhancing the feasibility of the quantum-based solution of the dispersion problem.","PeriodicalId":100644,"journal":{"name":"IEEE Transactions on Quantum Engineering","volume":"5 ","pages":"1-16"},"PeriodicalIF":0.0,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10654547","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142324300","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":"Improving Probabilistic Error Cancellation in the Presence of Nonstationary Noise","authors":"Samudra Dasgupta;Travis S. Humble","doi":"10.1109/TQE.2024.3435757","DOIUrl":"https://doi.org/10.1109/TQE.2024.3435757","url":null,"abstract":"In this article, we investigate the stability of probabilistic error cancellation (PEC) outcomes in the presence of nonstationary noise, which is an obstacle to achieving accurate observable estimates. Leveraging Bayesian methods, we design a strategy to enhance PEC stability and accuracy. Our experiments using a five-qubit implementation of the Bernstein–Vazirani algorithm and conducted on the ibm_kolkata device reveal a 42% improvement in accuracy and a 60% enhancement in stability compared to nonadaptive PEC. These results underscore the importance of adaptive estimation processes to effectively address nonstationary noise, vital for advancing PEC utility.","PeriodicalId":100644,"journal":{"name":"IEEE Transactions on Quantum Engineering","volume":"5 ","pages":"1-14"},"PeriodicalIF":0.0,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10645687","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142117896","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":"Quantum Circuit for Imputation of Missing Data","authors":"Claudio Sanavio;Simone Tibaldi;Edoardo Tignone;Elisa Ercolessi","doi":"10.1109/TQE.2024.3447875","DOIUrl":"https://doi.org/10.1109/TQE.2024.3447875","url":null,"abstract":"The imputation of missing data is a common procedure in data analysis that consists in predicting missing values of incomplete data points. In this work, we analyze a variational quantum circuit for the imputation of missing data. We construct variational quantum circuits with gates complexity \u0000<inline-formula><tex-math>$mathcal {O}(N)$</tex-math></inline-formula>\u0000 and \u0000<inline-formula><tex-math>$mathcal {O}(N^{2})$</tex-math></inline-formula>\u0000 that return the last missing bit of a binary string for a specific distribution. We train and test the performance of the algorithms on a series of datasets finding good convergence of the results. Finally, we test the circuit for generalization to unseen data. For simple systems, we are able to describe the circuit analytically, making it possible to skip the tedious and unresolved problem of training the circuit with repetitive measurements. We find beforehand the optimal values of the parameters and make use of them to construct an optimal circuit suited to the generation of truly random data.","PeriodicalId":100644,"journal":{"name":"IEEE Transactions on Quantum Engineering","volume":"5 ","pages":"1-12"},"PeriodicalIF":0.0,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10643709","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368559","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}