Hilbert space dimension is a key resource for quantuminformationprocessing(1,2). Not only is a large overall Hilbert space an essential requirement for quantum error correction, but a large local Hilbert space can a...
Hilbert space dimension is a key resource for quantuminformationprocessing(1,2). Not only is a large overall Hilbert space an essential requirement for quantum error correction, but a large local Hilbert space can also be advantageous for realizing gates and algorithms more efficiently(3, 4, 5, 6-7). As a result, there has been considerable experimental effort in recent years to develop quantum computing platforms using qudits (d-dimensional quantum systems with d > 2) as the fundamental unit of quantuminformation(8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18-19). Just as with qubits, quantum error correction of these qudits will be necessary in the long run, but so far, error correction of logical qudits has not been demonstrated experimentally. Here we report the experimental realization of an error-corrected logical qutrit (d = 3) and ququart (d = 4), which was achieved with the Gottesman-Kitaev-Preskill bosonic code(20). Using a reinforcement learning agent(21,22), we optimized the Gottesman-Kitaev-Preskill qutrit (ququart) as a ternary (quaternary) quantum memory and achieved beyond break-even error correction with a gain of 1.82 +/- 0.03 (1.87 +/- 0.03). This work represents a novel way of leveraging the large Hilbert space of a harmonic oscillator to realize hardware-efficient quantum error correction.
Studies have demonstrated that a joined complete graph is a typical mathematical model that can support a fast quantum search. In this paper, we study the implementation of joined complete graphs in atomic systems and...
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Studies have demonstrated that a joined complete graph is a typical mathematical model that can support a fast quantum search. In this paper, we study the implementation of joined complete graphs in atomic systems and realize a quantum search of runtime ■ based on this implementation with a success probability of 50%. Even though the practical systems inevitably interact with the surrounding environment, we reveal that a successful quantum search can be realized through delicately engineering the environment itself. We consider that our study will bring about a feasible way to realize quantuminformationprocessing including quantumalgorithms in reality.
quantum image segmentation algorithm can use its quantum mechanism to rapidly segment the objects in a quantum image. However, the existing quantum image segmentation algorithms can only segment static objects in the ...
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quantum image segmentation algorithm can use its quantum mechanism to rapidly segment the objects in a quantum image. However, the existing quantum image segmentation algorithms can only segment static objects in the image and use more quantum resource(qubit). In this paper, a novel quantum segmentation algorithm based on background-difference method for NEQR image is proposed, which can segment dynamic objects in a static scene image by using fewer qubits. In addition, an efficient and feasible quantum absolute value subtractor is designed, which is an exponential improvement over the existing quantum absolute value subtractor. Then, a complete quantum circuit is designed to segment the NEQR image. For a 2(n) x 2(n) image with gray-scale range of [0,2(q)-1], the complexity of our algorithm is O(q), which has an exponential improvement over the classical segmentation algorithm, and the complexity will not increase as the image's size increases. The experiment is conducted on IBM Q to show the feasibility of our algorithm in the noisy intermediate-scale quantum (NISQ) era.
Advancements in quantum machine learning offer unprecedented potential to revolutionize financial portfolio optimization, maximizing returns while managing risks efficiently. This study focuses on advancing quantum ma...
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ISBN:
(纸本)9798350361186
Advancements in quantum machine learning offer unprecedented potential to revolutionize financial portfolio optimization, maximizing returns while managing risks efficiently. This study focuses on advancing quantum machine learning algorithms for optimal financial portfolio management, presenting a novel approach implemented in Python that outperforms existing methods. The algorithm's capability to generate such substantial returns over time positions it as a groundbreaking tool for portfolio optimization in the dynamic landscape of financial markets. In the pursuit of enhancing quantum machine learning algorithms, this research focuses on the development and optimization of the QSVM algorithm. Leveraging Python for implementation, the study considers critical factors such as quantum circuit optimization, noise mitigation, and the integration of classical and quantum components to achieve superior results. The achieved portfolio performance over time not only underscores the algorithm's efficacy but also signifies a quantum advantage in financial decision-making. The implementation in Python ensures accessibility and applicability, facilitating the integration of this advanced quantum algorithm into existing financial frameworks. This research contributes to the evolving field of quantum finance, showcasing the potential of quantum machine learning in optimizing financial portfolios. The findings not only validate the superior performance of the proposed QSVM but also highlight the broader implications for the future of financial decision support systems, where quantumalgorithms could play a transformative role in enhancing portfolio management strategies. The proposed quantum Support Vector Machine (QSVM) demonstrates unparalleled success, with a remarkable Portfolio performance over time of 89.65%. This result significantly surpasses existing quantumalgorithms, including quantum Principal Component Analysis (QPCA), quantum Boltzmann Machines (QBM), and quantum K
The Kagome lattice, a captivating lattice structure composed of interconnected triangles with frustrated magnetic properties, has garnered considerable interest in condensed matter physics, quantum magnetism, and quan...
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ISBN:
(纸本)9783031686160;9783031686177
The Kagome lattice, a captivating lattice structure composed of interconnected triangles with frustrated magnetic properties, has garnered considerable interest in condensed matter physics, quantum magnetism, and quantum computing. The Ansatz optimization provided in this study along with extensive research on optimization technique results us with high accuracy. This study focuses on using multiple ansatz models to create an effective Variational quantum Eigen-solver (VQE) on the Kagome lattice of Herbertsmithite. By comparing various optimization methods and optimizing the VQE ansatz models, the main goal is to estimate ground state attributes with high accuracy. This study advances quantum computing and advances our knowledge of quantum materials with complex lattice structures by taking advantage of the distinctive geometric configuration and features of the Kagome lattice. Aiming to improve the effectiveness and accuracy of VQE implementations, the study examines how Ansatz Modelling, quantum effects, and optimization techniques interact in VQE algorithm. The findings and understandings from this study provide useful direction for upcoming improvements in quantumalgorithms, quantum machine learning and the investigation of quantum materials on the Kagome Lattice.
Integrating quantumalgorithms with machine and deep learning models has emerged as a promising method for addressing medical image classification challenges. This integration can enhance speed and efficiency when per...
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ISBN:
(纸本)9781510679320;9781510679337
Integrating quantumalgorithms with machine and deep learning models has emerged as a promising method for addressing medical image classification challenges. This integration can enhance speed and efficiency when performing complex computations. However, hybrid quantum models, particularly on quantum Convolutional Neural Networks (QCNNs) face two significant drawbacks: the placement of the quantum convolutional layer before the model architecture and the lack of integration of the quantum layer within the training process. These disadvantages reduce the robustness and reproducibility of the models. This study proposes that integrates the quantum layer into the quantum layer to address these shortcomings. We present a comparative analysis between a hybrid quantum deep learning model, which includes a trainable quantum layer, and its classical counterpart for the classification of skin cancer dermatoscopic images. The hybrid model attains 0.7865 of accuracy, a recall of 0.7321, a precision of 0.7268, and an F1 Score of 0.7288, while the classical model reaches an accuracy, recall, precision, and F1 Score of 0.8510, 0.8472, 0.8495, and 0.8447. The hybrid model achieves comparable results to its classical counterpart and demonstrates the advantages of weight adjustment in quantum layers and their potential in improving medical imaging analysis.
Localization is critical to numerous applications. The performance of classical localization protocols is limited by the specific form of distance information and suffer from considerable ranging errors. This paper fo...
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ISBN:
(纸本)9798350344868;9798350344851
Localization is critical to numerous applications. The performance of classical localization protocols is limited by the specific form of distance information and suffer from considerable ranging errors. This paper foresees a new opportunity by utilizing the exceptional property of entangled quantum states to measure a linear combination of target-anchor distances. Specifically, we consider localization with quantum-based TDoA measurements. Classical TDoA ranging takes the difference of two separate measurements. Instead, quantum ranging allows TDoA estimation within a single measurement, thereby reducing the ranging errors. Numerical simulations demonstrate that the new quantum-based localization significantly outperforms conventional algorithms based on classical ranging, with over 50% gains on average.
As tweakable block ciphers from public permutations, tweakable Even-Mansour ciphers are widely used in disk sector encryption and data storage encryption. With the rapid improvement of computing power, especially the ...
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As tweakable block ciphers from public permutations, tweakable Even-Mansour ciphers are widely used in disk sector encryption and data storage encryption. With the rapid improvement of computing power, especially the development of quantum computing technology and quantum computers, the quantum security of tweakable Even-Mansour ciphers should be concerned and studied. This paper focuses on the security of tweakable Even-Mansour ciphers in the quantum setting. For one -round tweakable Even-Mansour cipher, we give its quantum circuit, present a quantum key recovery attack in polynomial time by Simon's algorithm and show the concrete resource estimation. For two -round tweakable Even-Mansour cipher, we present a better quantum key recovery attack by BHT -meets -Simon algorithm than that by Grover -meets -Simon algorithm from a new perspective of variable tweaks and show the concrete resource estimation. Finally, we generalize to r -round tweakable Even-Mansour cipher and present a quantum key recovery attack by combining Grover's algorithm and Simon's algorithm. Our work is of great importance. We use BHT -meets -Simon algorithm to achieve better quantum key recovery attacks than Grover -meets -Simon algorithm for the first time.
Classical algorithms for market equilibrium computation such as proportional response dynamics face scalability issues with Internet-based applications such as auctions, recommender systems, and fair division, despite...
With the rapid development of information technology, physical education information management system has become an important part of modern education. However, the traditional physical education information manageme...
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With the rapid development of information technology, physical education information management system has become an important part of modern education. However, the traditional physical education information management system has some problems in optical communication technology, such as bandwidth limitation and slow transmission speed, which affect the performance and effect of the system. This paper aims to improve the application of optical communication technology in physical education teaching information management system by using machine learning algorithm, and improve the performance and effect of the system. This paper collects the relevant data of PE teaching information management system, and carries on the pre-processing and feature extraction. The appropriate machine learning algorithm was then selected to train and optimize the model and applied to optical communication technology. The new system has higher bandwidth and transmission speed, can process and transmit data faster, and improve the teaching effect.
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