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KANQAS: Kolmogorov-Arnold Network for Quantum Architecture Search KANQAS:用于量子架构搜索的柯尔莫哥洛夫-阿诺德网络
IF 5.8 2区 物理与天体物理 Q1 OPTICS Pub Date : 2024-11-12 DOI: 10.1140/epjqt/s40507-024-00289-z
Akash Kundu, Aritra Sarkar, Abhishek Sadhu

Quantum architecture Search (QAS) is a promising direction for optimization and automated design of quantum circuits towards quantum advantage. Recent techniques in QAS emphasize Multi-Layer Perceptron (MLP)-based deep Q-networks. However, their interpretability remains challenging due to the large number of learnable parameters and the complexities involved in selecting appropriate activation functions. In this work, to overcome these challenges, we utilize the Kolmogorov-Arnold Network (KAN) in the QAS algorithm, analyzing their efficiency in the task of quantum state preparation and quantum chemistry. In quantum state preparation, our results show that in a noiseless scenario, the probability of success is 2× to 5× higher than MLPs. In noisy environments, KAN outperforms MLPs in fidelity when approximating these states, showcasing its robustness against noise. In tackling quantum chemistry problems, we enhance the recently proposed QAS algorithm by integrating curriculum reinforcement learning with a KAN structure. This facilitates a more efficient design of parameterized quantum circuits by reducing the number of required 2-qubit gates and circuit depth. Further investigation reveals that KAN requires a significantly smaller number of learnable parameters compared to MLPs; however, the average time of executing each episode for KAN is higher.

量子架构搜索(QAS)是优化和自动设计量子电路以实现量子优势的一个有前途的方向。最近的量子架构搜索技术强调基于多层感知器(MLP)的深度 Q 网络。然而,由于可学习参数的数量庞大,以及选择适当激活函数的复杂性,它们的可解释性仍然具有挑战性。在这项工作中,为了克服这些挑战,我们在 QAS 算法中使用了 Kolmogorov-Arnold 网络(KAN),分析了它们在量子态准备和量子化学任务中的效率。在量子态准备中,我们的结果表明,在无噪声环境下,KAN 的成功概率比 MLP 高 2 倍到 5 倍。在有噪声的环境中,KAN 在逼近这些状态时的保真度优于 MLP,显示了它对噪声的鲁棒性。在处理量子化学问题时,我们通过将课程强化学习与 KAN 结构相结合,增强了最近提出的 QAS 算法。这有助于通过减少所需的 2 量子位门数量和电路深度,更高效地设计参数化量子电路。进一步研究发现,与 MLPs 相比,KAN 所需的可学习参数数量要少得多;但是,KAN 执行每集的平均时间却更长。
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引用次数: 0
Teaching quantum information science to secondary school students with photon polarization and which-path encoding 用光子偏振和路径编码向中学生教授量子信息科学
IF 5.8 2区 物理与天体物理 Q1 OPTICS Pub Date : 2024-11-05 DOI: 10.1140/epjqt/s40507-024-00287-1
Giacomo Zuccarini, Claudio Sutrini, Maria Bondani, Chiara Macchiavello, Massimiliano Malgieri

Research and curriculum development on quantum information science is a novel but technologically and socially significant challenge for physics education. While the debate is open on the core content, the approaches, and the strategies for addressing the need of effective instruction on the subject-matter, some indications have begun to emerge. Among them, the importance of an earlier start of education and of helping students develop not only a theoretical knowledge, but also high-level experimental skills including ideal design and conduction of experiments. Such skills are challenging to attain in existing traditional programs and may be considered inaccessible at introductory level because of the difficulties connected with qubit implementations. Here we present the design process, the structure, and a preliminary evaluation of a course for secondary school that is aimed to promote the building of a basic but integrated understanding of quantum information science, including experimental design and lab activities. The course was developed within the model of educational reconstruction, and embedded into a conceptual change framework in physics and computation. The encoding of polarization and which-path information of a photon is used to engage students in the development of a global model of logical encoding and processing, in ideal experimental design of gates and circuits, and in their implementation on the optical bench. Data show the effectiveness of the course in promoting student engagement in the modelling of gates in different encodings, in fostering an understanding of the computational role of physical setups, and a positive attitude and interest towards quantum computation and innovative teaching methods.

量子信息科学的研究和课程开发是物理教育面临的一项新颖但在技术和社会方面意义重大的挑战。尽管对该学科的核心内容、教学方法和有效教学策略仍有争议,但一些迹象已经开始显现。其中,提早开始教育和帮助学生不仅掌握理论知识,而且培养高水平的实验技能,包括理想的设计和进行实验的重要性已经显现出来。这些技能在现有的传统课程中很难达到,而且由于与量子比特实现相关的困难,可能会被认为在入门阶段无法达到。在此,我们介绍一门中学课程的设计过程、结构和初步评估,该课程旨在促进学生建立对量子信息科学的基本但综合的理解,包括实验设计和实验活动。该课程是在教育重建模式下开发的,并嵌入了物理和计算的概念变革框架。通过对光子的偏振和路径信息进行编码,让学生参与逻辑编码和处理全局模型的开发、门电路的理想实验设计以及在光学工作台上的实现。数据显示,该课程有效地促进了学生参与不同编码的门建模,培养了学生对物理设置的计算作用的理解,以及对量子计算和创新教学方法的积极态度和兴趣。
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引用次数: 0
Generation of phonon quantum states and quantum correlations among single photon emitters in hexagonal boron nitride 六方氮化硼中声子量子态的产生和单光子发射器之间的量子相关性
IF 5.8 2区 物理与天体物理 Q1 OPTICS Pub Date : 2024-11-05 DOI: 10.1140/epjqt/s40507-024-00286-2
Hugo Molinares, Fernanda Pinilla, Enrique Muñoz, Francisco Muñoz, Vitalie Eremeev

Hexagonal boron nitride exhibits two types of defects with great potential for quantum information technologies: single-photon emitters (SPEs) and one-dimensional grain boundaries hosting topologically-protected phonons, termed as topologically-protected phonon lines (TPL). Here, by means of a simple effective model and density functional theory calculations, we show that it is possible to use these phonons for the transmission of information. Particularly, a single SPE can be used to induce single-, two- and qubit-phonon states in the one-dimensional channel, and (ii) two distant SPEs can be coupled by the TPL that acts as a waveguide, thus exhibiting strong quantum correlations. We highlight the possibilities offered by this material-built-in nano-architecture as a phononic device for quantum information technologies.

六方氮化硼具有两类对量子信息技术具有巨大潜力的缺陷:单光子发射器(SPE)和容纳拓扑保护声子(称为拓扑保护声子线(TPL))的一维晶界。在这里,通过一个简单有效的模型和密度泛函理论计算,我们证明了利用这些声子进行信息传输是可能的。特别是,单个 SPE 可用于在一维通道中诱导单比特、双比特和比特声子态;(ii) 两个相距甚远的 SPE 可通过充当波导的 TPL 耦合,从而表现出很强的量子相关性。我们强调了这种材料内置纳米结构作为量子信息技术的声子器件所带来的可能性。
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引用次数: 0
A computational study and analysis of Variational Quantum Eigensolver over multiple parameters for molecules and ions 分子和离子多参数变量量子求解器的计算研究与分析
IF 5.8 2区 物理与天体物理 Q1 OPTICS Pub Date : 2024-10-28 DOI: 10.1140/epjqt/s40507-024-00280-8
Ashwin Sivakumar, Harishankar K Nair, Aurum Joshi, Kenson Wesley R, Akash P Videsh, Reena Monica P

Material discovery is a phenomenon practiced since the evolution of the world. The discovery of materials has led to significant development in varied fields such as Science, Engineering and Technology. Computationally simulating molecules has been an area of interest in the industry as well as academia. However, simulating large molecules can be computationally expensive in terms of computing power and complexity. Quantum computing is a recent development that can improve the efficiency in predicting properties of atoms and molecules which will be useful for material design. The Variational Quantum Eigensolver (VQE) is one such quantum algorithm used to calculate the ground state energy of molecules or ions. In this study, we have done a comparative analysis of the parameters that constitute the VQE algorithm. This includes components such as basis, qubit mapping, ansatz, and optimizers used. We have also developed a database consisting of 79 single atoms and their variations of oxidation states and 33 molecules with the data of their Hamiltonian and ground state energy and dipole moment.

材料发现是世界进化以来一直存在的现象。材料的发现推动了科学、工程和技术等各个领域的重大发展。计算模拟分子一直是工业界和学术界感兴趣的领域。然而,模拟大分子在计算能力和复杂性方面都非常昂贵。量子计算是最近的一项发展,它可以提高预测原子和分子特性的效率,这对材料设计非常有用。变分量子求解器(VQE)就是这样一种用于计算分子或离子基态能量的量子算法。在这项研究中,我们对构成 VQE 算法的参数进行了比较分析。这包括所使用的基础、量位映射、解析和优化器等组成部分。我们还开发了一个数据库,其中包括 79 个单个原子及其氧化态变化,以及 33 个分子及其哈密顿、基态能量和偶极矩数据。
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引用次数: 0
Quantum data encoding: a comparative analysis of classical-to-quantum mapping techniques and their impact on machine learning accuracy 量子数据编码:经典到量子映射技术的比较分析及其对机器学习准确性的影响
IF 5.8 2区 物理与天体物理 Q1 OPTICS Pub Date : 2024-10-25 DOI: 10.1140/epjqt/s40507-024-00285-3
Minati Rath, Hema Date

This study explores the integration of quantum data embedding techniques into classical machine learning (ML) algorithms; to assess performance enhancements and computational implications across a spectrum of models. We explored various classical-to-quantum mapping methods; ranging from basis encoding and angle encoding to amplitude encoding; for encoding classical data. We conducted an extensive empirical study encompassing popular ML algorithms, including Logistic Regression, K-Nearest Neighbors, Support Vector Machines, and ensemble methods like Random Forest, LightGBM, AdaBoost, and CatBoost. Our findings reveal that quantum data embedding contributes to improved classification accuracy and F1 scores, particularly notable in models that inherently benefit from enhanced feature representation. We observed nuanced effects on running time, with low-complexity models exhibiting moderate increases and more computationally intensive models experiencing discernible changes. Notably, ensemble methods demonstrated a favorable balance between performance gains and computational overhead.

This study underscores the potential of quantum data embedding to enhance classical ML models and emphasizes the importance of weighing performance improvements against computational costs. Future research may involve refining quantum encoding processes to optimize computational efficiency and explore scalability for real-world applications. Our work contributes to the growing body of knowledge on the intersection of quantum computing and classical machine learning, offering insights for researchers and practitioners seeking to harness the advantages of quantum-inspired techniques in practical scenarios.

本研究探索将量子数据嵌入技术整合到经典机器学习(ML)算法中,以评估各种模型的性能提升和计算影响。我们探索了各种经典到量子的映射方法,从基础编码、角度编码到振幅编码,用于编码经典数据。我们进行了广泛的实证研究,涵盖了流行的 ML 算法,包括 Logistic 回归、K-Nearest Neighbors、支持向量机,以及随机森林、LightGBM、AdaBoost 和 CatBoost 等集合方法。我们的研究结果表明,量子数据嵌入有助于提高分类准确率和 F1 分数,这在本质上得益于增强特征表示的模型中尤为明显。我们观察到了量子数据嵌入对运行时间的细微影响,低复杂度模型表现出适度的增长,而计算密集型模型则经历了明显的变化。这项研究强调了量子数据嵌入增强经典 ML 模型的潜力,并强调了权衡性能提升与计算成本的重要性。未来的研究可能涉及改进量子编码过程,以优化计算效率,并探索实际应用的可扩展性。我们的工作为量子计算与经典机器学习交叉领域不断增长的知识库做出了贡献,为研究人员和从业人员在实际应用场景中利用量子启发技术的优势提供了启示。
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引用次数: 0
An advanced quantum support vector machine for power quality disturbance detection and identification 用于电能质量干扰检测和识别的先进量子支持向量机
IF 5.8 2区 物理与天体物理 Q1 OPTICS Pub Date : 2024-10-22 DOI: 10.1140/epjqt/s40507-024-00283-5
Qing-Le Wang, Yu Jin, Xin-Hao Li, Yue Li, Yuan-Cheng Li, Ke-Jia Zhang, Hao Liu, Long Cheng

Quantum algorithms have demonstrated extraordinary potential across numerous fields, offering significant advantages in solving practical problems. Power Quality Disturbances (PQDs) have always been a critical factor affecting the stability and safety of electrical power systems, and accurately detecting and identifying PQDs is crucial for ensuring reliable system operation. This paper explores the application of quantum algorithms in the field of power quality and proposes a novel method using Quantum Support Vector Machines (QSVM) to detect and identify PQDs, which marks the first application of QSVM in PQD analysis. The QSVM model employed involves three main stages: quantum feature mapping, quantum kernel computation, and model training. Quantum feature mapping uses quantum circuits to map classical data into a high-dimensional Hilbert space, enhancing feature separability. Quantum kernel computation calculates the inner products between features for model training. Rigorous theoretical and experimental analyses validate our approach. This method achieves a time complexity of (O(N^{2} log (N))), superior to classical SVM algorithms. Simulation results show high accuracy in PQDs detection, achieving a 100% detection rate and a 96.25% accuracy rate in single PQD identification. Experimental outcomes demonstrate robustness, maintaining over 87% accuracy even with increased noise levels, confirming its effectiveness in PQDs detection and identification.

量子算法已在众多领域展现出非凡的潜力,在解决实际问题方面具有显著优势。电能质量干扰(PQD)一直是影响电力系统稳定性和安全性的关键因素,准确检测和识别 PQD 对于确保系统可靠运行至关重要。本文探讨了量子算法在电能质量领域的应用,并提出了一种利用量子支持向量机(QSVM)检测和识别 PQD 的新方法,这标志着 QSVM 在 PQD 分析中的首次应用。所采用的 QSVM 模型包括三个主要阶段:量子特征映射、量子核计算和模型训练。量子特征映射利用量子电路将经典数据映射到高维希尔伯特空间,从而提高特征的可分离性。量子核计算计算特征之间的内积,用于模型训练。严格的理论和实验分析验证了我们的方法。该方法的时间复杂度为(O(N^{2}log (N)log(N)),优于经典的 SVM 算法。仿真结果表明,PQD 的检测准确率很高,检测率达到 100%,单个 PQD 识别的准确率达到 96.25%。实验结果证明了该算法的鲁棒性,即使在噪声水平增加的情况下也能保持 87% 以上的准确率,从而证实了它在 PQDs 检测和识别方面的有效性。
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引用次数: 0
A methodology to select and adjust quantum noise models through emulators: benchmarking against real backends 通过仿真器选择和调整量子噪声模型的方法:以真实后端为基准
IF 5.8 2区 物理与天体物理 Q1 OPTICS Pub Date : 2024-10-22 DOI: 10.1140/epjqt/s40507-024-00284-4
J. A. Bravo-Montes, Miriam Bastante, Guillermo Botella, Alberto del Barrio, F. García-Herrero

Currently, access to quantum processors is costly in terms of time, and power. There are quantum simulators and emulators on the market that offer alternatives for evaluating the behavior of a real quantum processor. However, these emulation environments present accuracy deviations from real devices, mainly because of difficult-to-model error sources. In this study, a methodology is proposed that allows the selection of noise models and adjustment of their parameters, considering the nature of the backends (technology, topology, vendor, model, etc.). The proposed methodology is illustrated using a small superconducting example based on the ibm_perth backend (seven qubits) and a comparison between the quantum emulators Qaptiva and Qiskit, where six different noise models are applied, achieving a fidelity deviation of 0.686% at best with respect to the real device.

目前,使用量子处理器需要耗费大量时间和电力。市场上的量子模拟器和仿真器为评估真实量子处理器的行为提供了替代方案。然而,这些仿真环境与真实设备存在精度偏差,主要原因是难以模拟误差源。本研究提出了一种方法,可根据后端设备的性质(技术、拓扑结构、供应商、模型等)选择噪声模型并调整其参数。我们使用一个基于 ibm_perth 后端(7 个量子位)的小型超导示例,以及量子仿真器 Qaptiva 和 Qiskit 之间的比较来说明所提出的方法,其中应用了 6 种不同的噪声模型,与真实设备的保真度偏差最多为 0.686%。
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引用次数: 0
Trainability maximization using estimation of distribution algorithms assisted by surrogate modelling for quantum architecture search 利用代用建模辅助的分布估计算法实现可训练性最大化,用于量子架构搜索
IF 5.8 2区 物理与天体物理 Q1 OPTICS Pub Date : 2024-10-18 DOI: 10.1140/epjqt/s40507-024-00282-6
Vicente P. Soloviev, Vedran Dunjko, Concha Bielza, Pedro Larrañaga, Hao Wang

Quantum architecture search (QAS) involves optimizing both the quantum parametric circuit configuration but also its parameters for a variational quantum algorithm. Thus, the problem is known to be multi-level as the performance of a given architecture is unknown until its parameters are tuned using classical routines. Moreover, the task becomes even more complicated since well-known trainability issues, e.g., barren plateaus (BPs), can occur. In this paper, we aim to achieve two improvements in QAS: (1) to reduce the number of measurements by an online surrogate model of the evaluation process that aggressively discards architectures of poor performance; (2) to avoid training the circuits when BPs are present. To detect the presence of the BPs, we employed a recently developed metric, information content, which only requires measuring the energy values of a small set of parameters to estimate the magnitude of cost function’s gradient. The main idea of this proposal is to leverage a recently developed metric which can be used to detect the onset of vanishing gradients to ensure the overall search avoids such unfavorable regions. We experimentally validate our proposal for the variational quantum eigensolver and showcase that our algorithm is able to find solutions that have been previously proposed in the literature for the Hamiltonians; but also to outperform the state of the art when initializing the method from the set of architectures proposed in the literature. The results suggest that the proposed methodology could be used in environments where it is desired to improve the trainability of known architectures while maintaining good performance.

量子架构搜索(QAS)不仅涉及优化量子参数电路配置,还涉及优化变量子算法的参数。因此,这个问题是多层次的,因为在使用经典程序调整参数之前,给定架构的性能是未知的。此外,由于可能出现众所周知的可训练性问题,如贫瘠高原(BP),因此任务变得更加复杂。在本文中,我们的目标是实现 QAS 的两项改进:(1) 通过评估过程的在线代理模型减少测量次数,该模型会主动放弃性能较差的架构;(2) 避免在出现 BP 时对电路进行训练。为了检测 BPs 的存在,我们采用了最近开发的一种指标--信息含量,它只需要测量一小部分参数的能量值,就能估算出成本函数梯度的大小。这项建议的主要思路是利用最近开发的指标来检测梯度消失的起始点,以确保整体搜索避开此类不利区域。我们通过实验验证了我们针对变分量子等差数列求解器提出的建议,并表明我们的算法不仅能找到以前文献中提出的哈密尔顿解,而且在从文献中提出的架构集初始化方法时,其性能也优于目前的技术水平。结果表明,所提出的方法可用于希望提高已知架构的可训练性,同时保持良好性能的环境中。
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引用次数: 0
Correction: Keep it secret, keep it safe: teaching quantum key distribution in high school 更正:保密、安全:高中量子密钥分配教学
IF 5.8 2区 物理与天体物理 Q1 OPTICS Pub Date : 2024-10-18 DOI: 10.1140/epjqt/s40507-024-00281-7
Efraim Yehuda Weissman, Avraham Merzel, Nadav Katz, Igal Galili
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引用次数: 0
Fault-tolerant double-circular connectivity pattern for quantum stabilizer codes 量子稳定器代码的容错双环连接模式
IF 5.8 2区 物理与天体物理 Q1 OPTICS Pub Date : 2024-10-17 DOI: 10.1140/epjqt/s40507-024-00278-2
Chao Du, Zhi Ma, Yiting Liu, Hong Wang, Yangyang Fei

Recently, the circular connectivity pattern has been presented for a class of stabilizer quantum error correction codes. The circular connectivity pattern for such a class of stabilizer codes can be implemented in a resource-efficient manner using a single ancilla and native two-qubit Controlled-Not-Swap gates (CNS) gates, which may be interesting for demonstrating error-correction codes with superconducting quantum processors. However, one concern is that this scheme is not fault-tolerant. And it might not apply to the Calderbank-Shor-Steane (CSS) codes. In this paper, we present a fault-tolerant version of the circular connectivity pattern, named the double-circular connectivity pattern. This pattern is an implementation for syndrome-measurement circuits with a flagged error correction scheme for stabilizer codes. We illustrate that this pattern is available for Steane code (a CSS code), Laflamme’s five-qubit code, and Shor’s nine-qubit code. For Laflamme’s five-qubit code and Shor’s nine-qubit code, the pattern has the property that it uses only native two-qubit CNS gates, which are more efficient in the superconducting quantum platform.

最近,有人提出了一类稳定器量子纠错码的循环连接模式。这类稳定器纠错码的环形连通模式可以通过使用单个ancilla和本地双量子比特受控不交换门(CNS)以节省资源的方式实现,这对于用超导量子处理器演示纠错码可能很有意义。然而,令人担忧的是,这种方案不具有容错性。而且它可能不适用于 Calderbank-Shor-Steane (CSS) 代码。在本文中,我们提出了圆形连接模式的容错版本,命名为双圆形连接模式。这种模式是综合征测量电路的一种实现方式,具有稳定器代码的标记纠错方案。我们举例说明,这种模式适用于 Steane 码(一种 CSS 码)、Laflamme 的五量子比特码和 Shor 的九量子比特码。对于 Laflamme 的五量子比特码和 Shor 的九量子比特码,该模式具有只使用本地二量子比特 CNS 门的特性,这在超导量子平台中更为高效。
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引用次数: 0
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