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.
{"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}
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。
{"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}
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}
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 $mathcal {O}(N)$ and $mathcal {O}(N^{2})$ 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.
{"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}
Pub Date : 2024-08-20DOI: 10.1109/TQE.2024.3445967
Fabian Hader;Sarah Fleitmann;Jan Vogelbruch;Lotte Geck;Stefan van Waasen
Quantum dots (QDs) must be tuned precisely to provide a suitable basis for quantum computation. A scalable platform for quantum computing can only be achieved by fully automating the tuning process. One crucial step is to trap the appropriate number of electrons in the QDs, typically accomplished by analyzing charge stability diagrams (CSDs). Training and testing automation algorithms require large amounts of data, which can be either measured and manually labeled in an experiment or simulated. This article introduces a new approach to the realistic simulation of such measurements. Our flexible framework enables the simulation of ideal CSD data complemented with appropriate sensor responses and distortions. We suggest using this simulation to benchmark published algorithms. Also, we encourage the extension by custom models and parameter sets to drive the development of robust technology-independent algorithms.
{"title":"Simulation of Charge Stability Diagrams for Automated Tuning Solutions (SimCATS)","authors":"Fabian Hader;Sarah Fleitmann;Jan Vogelbruch;Lotte Geck;Stefan van Waasen","doi":"10.1109/TQE.2024.3445967","DOIUrl":"https://doi.org/10.1109/TQE.2024.3445967","url":null,"abstract":"Quantum dots (QDs) must be tuned precisely to provide a suitable basis for quantum computation. A scalable platform for quantum computing can only be achieved by fully automating the tuning process. One crucial step is to trap the appropriate number of electrons in the QDs, typically accomplished by analyzing charge stability diagrams (CSDs). Training and testing automation algorithms require large amounts of data, which can be either measured and manually labeled in an experiment or simulated. This article introduces a new approach to the realistic simulation of such measurements. Our flexible framework enables the simulation of ideal CSD data complemented with appropriate sensor responses and distortions. We suggest using this simulation to benchmark published algorithms. Also, we encourage the extension by custom models and parameter sets to drive the development of robust technology-independent algorithms.","PeriodicalId":100644,"journal":{"name":"IEEE Transactions on Quantum Engineering","volume":"5 ","pages":"1-14"},"PeriodicalIF":0.0,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10640289","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142165056","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 : 2024-08-14DOI: 10.1109/TQE.2024.3443660
Jonathan Wurtz;Stefan H. Sack;Sheng-Tao Wang
Combinatorial optimization is a challenging problem applicable in a wide range of fields from logistics to finance. Recently, quantum computing has been used to attempt to solve these problems using a range of algorithms, including parameterized quantum circuits, adiabatic protocols, and quantum annealing. These solutions typically have several challenges: 1) there is little to no performance gain over classical methods; 2) not all constraints and objectives may be efficiently encoded in the quantum ansatz; and 3) the solution domain of the objective function may not be the same as the bit strings of measurement outcomes. This work presents “nonnative hybrid algorithms”: a framework to overcome these challenges by integrating quantum and classical resources with a hybrid approach. By designing nonnative quantum variational anosatzes that inherit some but not all problem structure, measurement outcomes from the quantum computer can act as a resource to be used by classical routines to indirectly compute optimal solutions, partially overcoming the challenges of contemporary quantum optimization approaches. These methods are demonstrated using a publicly available neutral-atom quantum computer on two simple problems of Max $k$ -Cut and maximum independent set. We find improvements in solution quality when comparing the hybrid algorithm to its “no quantum” version, a demonstration of a “comparative advantage.”
{"title":"Solving Nonnative Combinatorial Optimization Problems Using Hybrid Quantum–Classical Algorithms","authors":"Jonathan Wurtz;Stefan H. Sack;Sheng-Tao Wang","doi":"10.1109/TQE.2024.3443660","DOIUrl":"https://doi.org/10.1109/TQE.2024.3443660","url":null,"abstract":"Combinatorial optimization is a challenging problem applicable in a wide range of fields from logistics to finance. Recently, quantum computing has been used to attempt to solve these problems using a range of algorithms, including parameterized quantum circuits, adiabatic protocols, and quantum annealing. These solutions typically have several challenges: 1) there is little to no performance gain over classical methods; 2) not all constraints and objectives may be efficiently encoded in the quantum ansatz; and 3) the solution domain of the objective function may not be the same as the bit strings of measurement outcomes. This work presents “nonnative hybrid algorithms”: a framework to overcome these challenges by integrating quantum and classical resources with a hybrid approach. By designing nonnative quantum variational anosatzes that inherit some but not all problem structure, measurement outcomes from the quantum computer can act as a resource to be used by classical routines to indirectly compute optimal solutions, partially overcoming the challenges of contemporary quantum optimization approaches. These methods are demonstrated using a publicly available neutral-atom quantum computer on two simple problems of Max \u0000<inline-formula><tex-math>$k$</tex-math></inline-formula>\u0000-Cut and maximum independent set. We find improvements in solution quality when comparing the hybrid algorithm to its “no quantum” version, a demonstration of a “comparative advantage.”","PeriodicalId":100644,"journal":{"name":"IEEE Transactions on Quantum Engineering","volume":"5 ","pages":"1-14"},"PeriodicalIF":0.0,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10636813","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142447172","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 : 2024-08-09DOI: 10.1109/TQE.2024.3441229
John D. Malcolm;Alexander Roth;Mladjan Radic;Pablo Martín-Ramiro;Jon Oillarburu;Borja Aizpurua;Román Orús;Samuel Mugel
In this article, we apply a quantum optimization algorithm to solve a combinatorial problem with significant practical relevance occurring in clutch manufacturing. It is demonstrated how quantum optimization can play a role in real industrial applications in the manufacturing sector. Using the quantum annealer provided by D-Wave Systems, we analyze the performance of the quantum and quantum–classical hybrid solvers and compare them to deterministic- and random-algorithm classical benchmark solvers. The continued evolution of the quantum technology, indicating an expectation for even greater relevance in the future, is discussed, and the revolutionary potential it could have in the manufacturing sector is highlighted.
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