In this article, we study a Grover-type method for quadratic unconstrained binary optimization (QUBO) problems. For an $n$ -dimensional QUBO problem with $m$ nonzero terms, we construct a marker oracle for such problems with a tunable parameter, $Lambda in [ 1, m ] cap mathbb {Z}$ . At $d in mathbb {Z}_+$ precision, the oracle uses $O (n + Lambda d)$ qubits and has total depth of $O (frac{m}{Lambda } log _{2} (n) + log _{2} (d))$ and a non-Clifford depth of $O (frac{m}{Lambda })$ . Moreover, each qubit is required to be connected to at most $O (log _{2} (Lambda + d))$ other qubits. In the case of a maximum graph cuts, as $d = 2 leftlceil log _{2} (n) rightrceil$ always suffices, the depth of the marker oracle can be made as shallow as $O (log _{2} (n))$ . For all values of $Lambda$ , the non-Clifford gate count of these oracles is strictly lower (at least by a factor of $sim 2$ ) than previous constructions. Furthermore, we introduce a novel fixed-point Grover adaptive search for QUBO problems, using our oracle design and a hybrid fixed-point Grover search, motivated by the works of Boyer et al. (1988) and Li et al. (2019). This method has better performance guarantees than previous Grover adaptive search methods. Some of our results are novel and useful for any method based on the fixed-point Grover search. Finally, we give a heuristic argument that, with high probability and in $O (frac{log _{2} (n)}{sqrt{epsilon }})$ time, this adaptive method finds a configuration that is among the best $epsilon 2^{n}$ ones.
{"title":"Fixed-Point Grover Adaptive Search for Quadratic Binary Optimization Problems","authors":"Ákos Nagy;Jaime Park;Cindy Zhang;Atithi Acharya;Alex Khan","doi":"10.1109/TQE.2024.3484650","DOIUrl":"https://doi.org/10.1109/TQE.2024.3484650","url":null,"abstract":"In this article, we study a Grover-type method for quadratic unconstrained binary optimization (QUBO) problems. For an \u0000<inline-formula><tex-math>$n$</tex-math></inline-formula>\u0000-dimensional QUBO problem with \u0000<inline-formula><tex-math>$m$</tex-math></inline-formula>\u0000 nonzero terms, we construct a marker oracle for such problems with a tunable parameter, \u0000<inline-formula><tex-math>$Lambda in [ 1, m ] cap mathbb {Z}$</tex-math></inline-formula>\u0000. At \u0000<inline-formula><tex-math>$d in mathbb {Z}_+$</tex-math></inline-formula>\u0000 precision, the oracle uses \u0000<inline-formula><tex-math>$O (n + Lambda d)$</tex-math></inline-formula>\u0000 qubits and has total depth of \u0000<inline-formula><tex-math>$O (frac{m}{Lambda } log _{2} (n) + log _{2} (d))$</tex-math></inline-formula>\u0000 and a non-Clifford depth of \u0000<inline-formula><tex-math>$O (frac{m}{Lambda })$</tex-math></inline-formula>\u0000. Moreover, each qubit is required to be connected to at most \u0000<inline-formula><tex-math>$O (log _{2} (Lambda + d))$</tex-math></inline-formula>\u0000 other qubits. In the case of a maximum graph cuts, as \u0000<inline-formula><tex-math>$d = 2 leftlceil log _{2} (n) rightrceil$</tex-math></inline-formula>\u0000 always suffices, the depth of the marker oracle can be made as shallow as \u0000<inline-formula><tex-math>$O (log _{2} (n))$</tex-math></inline-formula>\u0000. For all values of \u0000<inline-formula><tex-math>$Lambda$</tex-math></inline-formula>\u0000, the non-Clifford gate count of these oracles is strictly lower (at least by a factor of \u0000<inline-formula><tex-math>$sim 2$</tex-math></inline-formula>\u0000) than previous constructions. Furthermore, we introduce a novel fixed-point Grover adaptive search for QUBO problems, using our oracle design and a hybrid fixed-point Grover search, motivated by the works of Boyer et al. (1988) and Li et al. (2019). This method has better performance guarantees than previous Grover adaptive search methods. Some of our results are novel and useful for any method based on the fixed-point Grover search. Finally, we give a heuristic argument that, with high probability and in \u0000<inline-formula><tex-math>$O (frac{log _{2} (n)}{sqrt{epsilon }})$</tex-math></inline-formula>\u0000 time, this adaptive method finds a configuration that is among the best \u0000<inline-formula><tex-math>$epsilon 2^{n}$</tex-math></inline-formula>\u0000 ones.","PeriodicalId":100644,"journal":{"name":"IEEE Transactions on Quantum Engineering","volume":"5 ","pages":"1-12"},"PeriodicalIF":0.0,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10726869","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142679411","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 : 2024-10-16DOI: 10.1109/TQE.2024.3476009
Mohadeseh Azari;Paul Polakos;Kaushik P. Seshadreesan
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.
{"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}
Pub Date : 2024-10-15DOI: 10.1109/TQE.2024.3481280
Yifeng Peng;Xinyi Li;Zhiding Liang;Ying Wang
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.
{"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}
Pub Date : 2024-10-09DOI: 10.1109/TQE.2024.3476929
Steven Herbert;Ifan Williams;Roland Guichard;Darren Ng
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.
在本文中，基于一些简单合理的假设，我们推导出了量子振幅估计的高斯噪声模型。我们提供了在各种 IBM 超导量子计算机和 Quantinuum 的 H1 捕获离子量子计算机上运行的量子振幅估计结果，表明所提出的模型与真实世界的实验数据非常吻合。我们还表明，所提出的高斯噪声模型可以很容易地与其他噪声模型组成，以捕捉高斯噪声无法很好描述的效应。我们给出了如何将该噪声模型嵌入任何无量子相位估计的量子振幅估计算法的通用程序，从而使振幅估计具有 "噪声意识"。然后，我们提供了利用 IBM 超导量子计算机的数据运行噪声感知量子振幅估算实现的实验结果，证明增加 "噪声感知 "可作为量子误差缓解的有效手段。
{"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}
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 $k$ -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.
{"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}
Pub Date : 2024-09-25DOI: 10.1109/TQE.2024.3467271
Namitha Liyanage;Yue Wu;Siona Tagare;Lin Zhong
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 $O(d^{3})$ . 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 $d$ , given $O(d^{3})$ parallel computing resources. The decoding time per measurement round decreases as $d$ 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 $d$ 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 $d=17$ 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 $d=51$ on a Xilinx VCU129 FPGA with an average latency of 544 ns per measurement round.
{"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}
Pub Date : 2024-09-19DOI: 10.1109/TQE.2024.3464572
Mohamed Shaban;Muhammad Ismail;Walid Saad
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.
{"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}
Pub Date : 2024-09-04DOI: 10.1109/TQE.2024.3454640
Tong Zhao;Bo Chen;Guanting Wu;Liang Zeng
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.
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Pub Date : 2024-08-28DOI: 10.1109/TQE.2024.3450852
Kein Yukiyoshi;Taku Mikuriya;Hyeon Seok Rou;Giuseppe Thadeu Freitas de Abreu;Naoki Ishikawa
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.
在这篇文章中，我们提出了最大和与最大-最小分散问题的新公式，可以通过格罗弗自适应搜索（GAS）量子算法求解，并提供二次加速。分散问题是被归类为 NP-困难的组合优化问题，经常出现在编码理论和涉及最优码本设计的无线通信应用中。而 GAS 是一种量子穷举搜索算法，可用于实现成熟的最大似然最优解。然而，在传统的天真公式中，通常依赖于二进制向量空间，导致搜索空间的大小甚至令 GAS 望而却步。为了规避这一挑战，我们转而在 Dicke 状态（具有相等汉明权重的二进制向量的相等叠加）上搜索最佳分散问题，这大大缩小了搜索空间，通过消除惩罚项简化了量子电路。此外，我们还提出了一种用等级取代距离系数的方法，有助于减少量子比特的数量。我们的分析表明，与使用哈达玛变换的传统 GAS 相比，所提出的技术降低了查询复杂度，增强了基于量子的色散问题解决方案的可行性。
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Pub Date : 2024-08-23DOI: 10.1109/TQE.2024.3435757
Samudra Dasgupta;Travis S. Humble
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.
{"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}
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